Diffuse Interface Methods for Multiclass Segmentation of High-Dimensional Data

DTIC Science & Technology

2014-03-04

handwritten digits , 1998. http://yann.lecun.com/exdb/mnist/. [19] S. Nene, S. Nayar, H. Murase, Columbia Object Image Library (COIL-100), Technical Report... recognition on smartphones using a multiclass hardware-friendly support vector machine, in: Ambient Assisted Living and Home Care, Springer, 2012, pp. 216–223.

BanglaLekha-Isolated: A multi-purpose comprehensive dataset of Handwritten Bangla Isolated characters.

PubMed

Biswas, Mithun; Islam, Rafiqul; Shom, Gautam Kumar; Shopon, Md; Mohammed, Nabeel; Momen, Sifat; Abedin, Anowarul

2017-06-01

BanglaLekha-Isolated, a Bangla handwritten isolated character dataset is presented in this article. This dataset contains 84 different characters comprising of 50 Bangla basic characters, 10 Bangla numerals and 24 selected compound characters. 2000 handwriting samples for each of the 84 characters were collected, digitized and pre-processed. After discarding mistakes and scribbles, 1,66,105 handwritten character images were included in the final dataset. The dataset also includes labels indicating the age and the gender of the subjects from whom the samples were collected. This dataset could be used not only for optical handwriting recognition research but also to explore the influence of gender and age on handwriting. The dataset is publicly available at https://data.mendeley.com/datasets/hf6sf8zrkc/2.

Recognition of Telugu characters using neural networks.

PubMed

Sukhaswami, M B; Seetharamulu, P; Pujari, A K

1995-09-01

The aim of the present work is to recognize printed and handwritten Telugu characters using artificial neural networks (ANNs). Earlier work on recognition of Telugu characters has been done using conventional pattern recognition techniques. We make an initial attempt here of using neural networks for recognition with the aim of improving upon earlier methods which do not perform effectively in the presence of noise and distortion in the characters. The Hopfield model of neural network working as an associative memory is chosen for recognition purposes initially. Due to limitation in the capacity of the Hopfield neural network, we propose a new scheme named here as the Multiple Neural Network Associative Memory (MNNAM). The limitation in storage capacity has been overcome by combining multiple neural networks which work in parallel. It is also demonstrated that the Hopfield network is suitable for recognizing noisy printed characters as well as handwritten characters written by different "hands" in a variety of styles. Detailed experiments have been carried out using several learning strategies and results are reported. It is shown here that satisfactory recognition is possible using the proposed strategy. A detailed preprocessing scheme of the Telugu characters from digitized documents is also described.

The Characteristics of Binary Spike-Time-Dependent Plasticity in HfO2-Based RRAM and Applications for Pattern Recognition

NASA Astrophysics Data System (ADS)

Zhou, Zheng; Liu, Chen; Shen, Wensheng; Dong, Zhen; Chen, Zhe; Huang, Peng; Liu, Lifeng; Liu, Xiaoyan; Kang, Jinfeng

2017-04-01

A binary spike-time-dependent plasticity (STDP) protocol based on one resistive-switching random access memory (RRAM) device was proposed and experimentally demonstrated in the fabricated RRAM array. Based on the STDP protocol, a novel unsupervised online pattern recognition system including RRAM synapses and CMOS neurons is developed. Our simulations show that the system can efficiently compete the handwritten digits recognition task, which indicates the feasibility of using the RRAM-based binary STDP protocol in neuromorphic computing systems to obtain good performance.

Limited receptive area neural classifier for recognition of swallowing sounds using continuous wavelet transform.

PubMed

Makeyev, Oleksandr; Sazonov, Edward; Schuckers, Stephanie; Lopez-Meyer, Paulo; Melanson, Ed; Neuman, Michael

2007-01-01

In this paper we propose a sound recognition technique based on the limited receptive area (LIRA) neural classifier and continuous wavelet transform (CWT). LIRA neural classifier was developed as a multipurpose image recognition system. Previous tests of LIRA demonstrated good results in different image recognition tasks including: handwritten digit recognition, face recognition, metal surface texture recognition, and micro work piece shape recognition. We propose a sound recognition technique where scalograms of sound instances serve as inputs of the LIRA neural classifier. The methodology was tested in recognition of swallowing sounds. Swallowing sound recognition may be employed in systems for automated swallowing assessment and diagnosis of swallowing disorders. The experimental results suggest high efficiency and reliability of the proposed approach.

Signature Verification Using N-tuple Learning Machine.

PubMed

Maneechot, Thanin; Kitjaidure, Yuttana

2005-01-01

This research presents new algorithm for signature verification using N-tuple learning machine. The features are taken from handwritten signature on Digital Tablet (On-line). This research develops recognition algorithm using four features extraction, namely horizontal and vertical pen tip position(x-y position), pen tip pressure, and pen altitude angles. Verification uses N-tuple technique with Gaussian thresholding.

Sunspot drawings handwritten character recognition method based on deep learning

NASA Astrophysics Data System (ADS)

Zheng, Sheng; Zeng, Xiangyun; Lin, Ganghua; Zhao, Cui; Feng, Yongli; Tao, Jinping; Zhu, Daoyuan; Xiong, Li

2016-05-01

High accuracy scanned sunspot drawings handwritten characters recognition is an issue of critical importance to analyze sunspots movement and store them in the database. This paper presents a robust deep learning method for scanned sunspot drawings handwritten characters recognition. The convolution neural network (CNN) is one algorithm of deep learning which is truly successful in training of multi-layer network structure. CNN is used to train recognition model of handwritten character images which are extracted from the original sunspot drawings. We demonstrate the advantages of the proposed method on sunspot drawings provided by Chinese Academy Yunnan Observatory and obtain the daily full-disc sunspot numbers and sunspot areas from the sunspot drawings. The experimental results show that the proposed method achieves a high recognition accurate rate.

Neuromorphic Hardware Architecture Using the Neural Engineering Framework for Pattern Recognition.

PubMed

Wang, Runchun; Thakur, Chetan Singh; Cohen, Gregory; Hamilton, Tara Julia; Tapson, Jonathan; van Schaik, Andre

2017-06-01

We present a hardware architecture that uses the neural engineering framework (NEF) to implement large-scale neural networks on field programmable gate arrays (FPGAs) for performing massively parallel real-time pattern recognition. NEF is a framework that is capable of synthesising large-scale cognitive systems from subnetworks and we have previously presented an FPGA implementation of the NEF that successfully performs nonlinear mathematical computations. That work was developed based on a compact digital neural core, which consists of 64 neurons that are instantiated by a single physical neuron using a time-multiplexing approach. We have now scaled this approach up to build a pattern recognition system by combining identical neural cores together. As a proof of concept, we have developed a handwritten digit recognition system using the MNIST database and achieved a recognition rate of 96.55%. The system is implemented on a state-of-the-art FPGA and can process 5.12 million digits per second. The architecture and hardware optimisations presented offer high-speed and resource-efficient means for performing high-speed, neuromorphic, and massively parallel pattern recognition and classification tasks.

Maximum mutual information estimation of a simplified hidden MRF for offline handwritten Chinese character recognition

NASA Astrophysics Data System (ADS)

Xiong, Yan; Reichenbach, Stephen E.

1999-01-01

Understanding of hand-written Chinese characters is at such a primitive stage that models include some assumptions about hand-written Chinese characters that are simply false. So Maximum Likelihood Estimation (MLE) may not be an optimal method for hand-written Chinese characters recognition. This concern motivates the research effort to consider alternative criteria. Maximum Mutual Information Estimation (MMIE) is an alternative method for parameter estimation that does not derive its rationale from presumed model correctness, but instead examines the pattern-modeling problem in automatic recognition system from an information- theoretic point of view. The objective of MMIE is to find a set of parameters in such that the resultant model allows the system to derive from the observed data as much information as possible about the class. We consider MMIE for recognition of hand-written Chinese characters using on a simplified hidden Markov Random Field. MMIE provides improved performance improvement over MLE in this application.

Handwritten recognition of Tamil vowels using deep learning

NASA Astrophysics Data System (ADS)

Ram Prashanth, N.; Siddarth, B.; Ganesh, Anirudh; Naveen Kumar, Vaegae

2017-11-01

We come across a large volume of handwritten texts in our daily lives and handwritten character recognition has long been an important area of research in pattern recognition. The complexity of the task varies among different languages and it so happens largely due to the similarity between characters, distinct shapes and number of characters which are all language-specific properties. There have been numerous works on character recognition of English alphabets and with laudable success, but regional languages have not been dealt with very frequently and with similar accuracies. In this paper, we explored the performance of Deep Belief Networks in the classification of Handwritten Tamil vowels, and conclusively compared the results obtained. The proposed method has shown satisfactory recognition accuracy in light of difficulties faced with regional languages such as similarity between characters and minute nuances that differentiate them. We can further extend this to all the Tamil characters.

Recognition of Similar Shaped Handwritten Marathi Characters Using Artificial Neural Network

NASA Astrophysics Data System (ADS)

Jane, Archana P.; Pund, Mukesh A.

2012-03-01

The growing need have handwritten Marathi character recognition in Indian offices such as passport, railways etc has made it vital area of a research. Similar shape characters are more prone to misclassification. In this paper a novel method is provided to recognize handwritten Marathi characters based on their features extraction and adaptive smoothing technique. Feature selections methods avoid unnecessary patterns in an image whereas adaptive smoothing technique form smooth shape of charecters.Combination of both these approaches leads to the better results. Previous study shows that, no one technique achieves 100% accuracy in handwritten character recognition area. This approach of combining both adaptive smoothing & feature extraction gives better results (approximately 75-100) and expected outcomes.

What differs in visual recognition of handwritten vs. printed letters? An fMRI study.

PubMed

Longcamp, Marieke; Hlushchuk, Yevhen; Hari, Riitta

2011-08-01

In models of letter recognition, handwritten letters are considered as a particular font exemplar, not qualitatively different in their processing from printed letters. Yet, some data suggest that recognizing handwritten letters might rely on distinct processes, possibly related to motor knowledge. We applied functional magnetic resonance imaging to compare the neural correlates of perceiving handwritten letters vs. standard printed letters. Statistical analysis circumscribed to frontal brain regions involved in hand-movement triggering and execution showed that processing of handwritten letters is supported by a stronger activation of the left primary motor cortex and the supplementary motor area. At the whole-brain level, additional differences between handwritten and printed letters were observed in the right superior frontal, middle occipital, and parahippocampal gyri, and in the left inferior precentral and the fusiform gyri. The results are suggested to indicate embodiment of the visual perception of handwritten letters. Copyright © 2010 Wiley-Liss, Inc.

Interpreting Chicken-Scratch: Lexical Access for Handwritten Words

ERIC Educational Resources Information Center

Barnhart, Anthony S.; Goldinger, Stephen D.

2010-01-01

Handwritten word recognition is a field of study that has largely been neglected in the psychological literature, despite its prevalence in society. Whereas studies of spoken word recognition almost exclusively employ natural, human voices as stimuli, studies of visual word recognition use synthetic typefaces, thus simplifying the process of word…

Automatic extraction of numeric strings in unconstrained handwritten document images

NASA Astrophysics Data System (ADS)

Haji, M. Mehdi; Bui, Tien D.; Suen, Ching Y.

2011-01-01

Numeric strings such as identification numbers carry vital pieces of information in documents. In this paper, we present a novel algorithm for automatic extraction of numeric strings in unconstrained handwritten document images. The algorithm has two main phases: pruning and verification. In the pruning phase, the algorithm first performs a new segment-merge procedure on each text line, and then using a new regularity measure, it prunes all sequences of characters that are unlikely to be numeric strings. The segment-merge procedure is composed of two modules: a new explicit character segmentation algorithm which is based on analysis of skeletal graphs and a merging algorithm which is based on graph partitioning. All the candidate sequences that pass the pruning phase are sent to a recognition-based verification phase for the final decision. The recognition is based on a coarse-to-fine approach using probabilistic RBF networks. We developed our algorithm for the processing of real-world documents where letters and digits may be connected or broken in a document. The effectiveness of the proposed approach is shown by extensive experiments done on a real-world database of 607 documents which contains handwritten, machine-printed and mixed documents with different types of layouts and levels of noise.

Comparison of crisp and fuzzy character networks in handwritten word recognition

NASA Technical Reports Server (NTRS)

Gader, Paul; Mohamed, Magdi; Chiang, Jung-Hsien

1992-01-01

Experiments involving handwritten word recognition on words taken from images of handwritten address blocks from the United States Postal Service mailstream are described. The word recognition algorithm relies on the use of neural networks at the character level. The neural networks are trained using crisp and fuzzy desired outputs. The fuzzy outputs were defined using a fuzzy k-nearest neighbor algorithm. The crisp networks slightly outperformed the fuzzy networks at the character level but the fuzzy networks outperformed the crisp networks at the word level.

Handwritten numeral databases of Indian scripts and multistage recognition of mixed numerals.

PubMed

Bhattacharya, Ujjwal; Chaudhuri, B B

2009-03-01

This article primarily concerns the problem of isolated handwritten numeral recognition of major Indian scripts. The principal contributions presented here are (a) pioneering development of two databases for handwritten numerals of two most popular Indian scripts, (b) a multistage cascaded recognition scheme using wavelet based multiresolution representations and multilayer perceptron classifiers and (c) application of (b) for the recognition of mixed handwritten numerals of three Indian scripts Devanagari, Bangla and English. The present databases include respectively 22,556 and 23,392 handwritten isolated numeral samples of Devanagari and Bangla collected from real-life situations and these can be made available free of cost to researchers of other academic Institutions. In the proposed scheme, a numeral is subjected to three multilayer perceptron classifiers corresponding to three coarse-to-fine resolution levels in a cascaded manner. If rejection occurred even at the highest resolution, another multilayer perceptron is used as the final attempt to recognize the input numeral by combining the outputs of three classifiers of the previous stages. This scheme has been extended to the situation when the script of a document is not known a priori or the numerals written on a document belong to different scripts. Handwritten numerals in mixed scripts are frequently found in Indian postal mails and table-form documents.

Learning in Stochastic Bit Stream Neural Networks.

PubMed

van Daalen, Max; Shawe-Taylor, John; Zhao, Jieyu

1996-08-01

This paper presents learning techniques for a novel feedforward stochastic neural network. The model uses stochastic weights and the "bit stream" data representation. It has a clean analysable functionality and is very attractive with its great potential to be implemented in hardware using standard digital VLSI technology. The design allows simulation at three different levels and learning techniques are described for each level. The lowest level corresponds to on-chip learning. Simulation results on three benchmark MONK's problems and handwritten digit recognition with a clean set of 500 16 x 16 pixel digits demonstrate that the new model is powerful enough for the real world applications. Copyright 1996 Elsevier Science Ltd

Dealing with contaminated datasets: An approach to classifier training

NASA Astrophysics Data System (ADS)

Homenda, Wladyslaw; Jastrzebska, Agnieszka; Rybnik, Mariusz

2016-06-01

The paper presents a novel approach to classification reinforced with rejection mechanism. The method is based on a two-tier set of classifiers. First layer classifies elements, second layer separates native elements from foreign ones in each distinguished class. The key novelty presented here is rejection mechanism training scheme according to the philosophy "one-against-all-other-classes". Proposed method was tested in an empirical study of handwritten digits recognition.

Spiking neural networks for handwritten digit recognition-Supervised learning and network optimization.

PubMed

Kulkarni, Shruti R; Rajendran, Bipin

2018-07-01

We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse biological spike rates below 300Hz achieves a classification accuracy of 98.17% on the MNIST test database with four times fewer parameters compared to the state-of-the-art. We present several insights from extensive numerical experiments regarding optimization of learning parameters and network configuration to improve its accuracy. We also describe a number of strategies to optimize the SNN for implementation in memory and energy constrained hardware, including approximations in computing the neuronal dynamics and reduced precision in storing the synaptic weights. Experiments reveal that even with 3-bit synaptic weights, the classification accuracy of the designed SNN does not degrade beyond 1% as compared to the floating-point baseline. Further, the proposed SNN, which is trained based on the precise spike timing information outperforms an equivalent non-spiking artificial neural network (ANN) trained using back propagation, especially at low bit precision. Thus, our study shows the potential for realizing efficient neuromorphic systems that use spike based information encoding and learning for real-world applications. Copyright © 2018 Elsevier Ltd. All rights reserved.

Handwritten Word Recognition Using Multi-view Analysis

NASA Astrophysics Data System (ADS)

de Oliveira, J. J.; de A. Freitas, C. O.; de Carvalho, J. M.; Sabourin, R.

This paper brings a contribution to the problem of efficiently recognizing handwritten words from a limited size lexicon. For that, a multiple classifier system has been developed that analyzes the words from three different approximation levels, in order to get a computational approach inspired on the human reading process. For each approximation level a three-module architecture composed of a zoning mechanism (pseudo-segmenter), a feature extractor and a classifier is defined. The proposed application is the recognition of the Portuguese handwritten names of the months, for which a best recognition rate of 97.7% was obtained, using classifier combination.

Learning representation hierarchies by sharing visual features: a computational investigation of Persian character recognition with unsupervised deep learning.

PubMed

Sadeghi, Zahra; Testolin, Alberto

2017-08-01

In humans, efficient recognition of written symbols is thought to rely on a hierarchical processing system, where simple features are progressively combined into more abstract, high-level representations. Here, we present a computational model of Persian character recognition based on deep belief networks, where increasingly more complex visual features emerge in a completely unsupervised manner by fitting a hierarchical generative model to the sensory data. Crucially, high-level internal representations emerging from unsupervised deep learning can be easily read out by a linear classifier, achieving state-of-the-art recognition accuracy. Furthermore, we tested the hypothesis that handwritten digits and letters share many common visual features: A generative model that captures the statistical structure of the letters distribution should therefore also support the recognition of written digits. To this aim, deep networks trained on Persian letters were used to build high-level representations of Persian digits, which were indeed read out with high accuracy. Our simulations show that complex visual features, such as those mediating the identification of Persian symbols, can emerge from unsupervised learning in multilayered neural networks and can support knowledge transfer across related domains.

Classification of remotely sensed data using OCR-inspired neural network techniques. [Optical Character Recognition

NASA Technical Reports Server (NTRS)

Kiang, Richard K.

1992-01-01

Neural networks have been applied to classifications of remotely sensed data with some success. To improve the performance of this approach, an examination was made of how neural networks are applied to the optical character recognition (OCR) of handwritten digits and letters. A three-layer, feedforward network, along with techniques adopted from OCR, was used to classify Landsat-4 Thematic Mapper data. Good results were obtained. To overcome the difficulties that are characteristic of remote sensing applications and to attain significant improvements in classification accuracy, a special network architecture may be required.

Numerical linear algebra in data mining

NASA Astrophysics Data System (ADS)

Eldén, Lars

Ideas and algorithms from numerical linear algebra are important in several areas of data mining. We give an overview of linear algebra methods in text mining (information retrieval), pattern recognition (classification of handwritten digits), and PageRank computations for web search engines. The emphasis is on rank reduction as a method of extracting information from a data matrix, low-rank approximation of matrices using the singular value decomposition and clustering, and on eigenvalue methods for network analysis.

Construction of language models for an handwritten mail reading system

NASA Astrophysics Data System (ADS)

Morillot, Olivier; Likforman-Sulem, Laurence; Grosicki, Emmanuèle

2012-01-01

This paper presents a system for the recognition of unconstrained handwritten mails. The main part of this system is an HMM recognizer which uses trigraphs to model contextual information. This recognition system does not require any segmentation into words or characters and directly works at line level. To take into account linguistic information and enhance performance, a language model is introduced. This language model is based on bigrams and built from training document transcriptions only. Different experiments with various vocabulary sizes and language models have been conducted. Word Error Rate and Perplexity values are compared to show the interest of specific language models, fit to handwritten mail recognition task.

Robust recognition of handwritten numerals based on dual cooperative network

NASA Technical Reports Server (NTRS)

Lee, Sukhan; Choi, Yeongwoo

1992-01-01

An approach to robust recognition of handwritten numerals using two operating parallel networks is presented. The first network uses inputs in Cartesian coordinates, and the second network uses the same inputs transformed into polar coordinates. How the proposed approach realizes the robustness to local and global variations of input numerals by handling inputs both in Cartesian coordinates and in its transformed Polar coordinates is described. The required network structures and its learning scheme are discussed. Experimental results show that by tracking only a small number of distinctive features for each teaching numeral in each coordinate, the proposed system can provide robust recognition of handwritten numerals.

Kannada character recognition system using neural network

NASA Astrophysics Data System (ADS)

Kumar, Suresh D. S.; Kamalapuram, Srinivasa K.; Kumar, Ajay B. R.

2013-03-01

Handwriting recognition has been one of the active and challenging research areas in the field of pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. As there is no sufficient number of works on Indian language character recognition especially Kannada script among 15 major scripts in India. In this paper an attempt is made to recognize handwritten Kannada characters using Feed Forward neural networks. A handwritten Kannada character is resized into 20x30 Pixel. The resized character is used for training the neural network. Once the training process is completed the same character is given as input to the neural network with different set of neurons in hidden layer and their recognition accuracy rate for different Kannada characters has been calculated and compared. The results show that the proposed system yields good recognition accuracy rates comparable to that of other handwritten character recognition systems.

Fuzzy Clustering of Multiple Instance Data

DTIC Science & Technology

2015-11-30

depth is not. To illustrate this data, in figure 1 we display the GPR signatures of the same mine buried at 3 in deep in two geographically different...target signature depends on the soil properties of the site. The same mine type is buried at 3in deep in both sites. Since its formal introduction...drug design [15], and the problem of handwritten digit recognition [16]. To the best of our knowledge, Diet - terich, et. al [1] were the first to

Development of an optical character recognition pipeline for handwritten form fields from an electronic health record.

PubMed

Rasmussen, Luke V; Peissig, Peggy L; McCarty, Catherine A; Starren, Justin

2012-06-01

Although the penetration of electronic health records is increasing rapidly, much of the historical medical record is only available in handwritten notes and forms, which require labor-intensive, human chart abstraction for some clinical research. The few previous studies on automated extraction of data from these handwritten notes have focused on monolithic, custom-developed recognition systems or third-party systems that require proprietary forms. We present an optical character recognition processing pipeline, which leverages the capabilities of existing third-party optical character recognition engines, and provides the flexibility offered by a modular custom-developed system. The system was configured and run on a selected set of form fields extracted from a corpus of handwritten ophthalmology forms. The processing pipeline allowed multiple configurations to be run, with the optimal configuration consisting of the Nuance and LEADTOOLS engines running in parallel with a positive predictive value of 94.6% and a sensitivity of 13.5%. While limitations exist, preliminary experience from this project yielded insights on the generalizability and applicability of integrating multiple, inexpensive general-purpose third-party optical character recognition engines in a modular pipeline.

Development of an optical character recognition pipeline for handwritten form fields from an electronic health record

PubMed Central

Peissig, Peggy L; McCarty, Catherine A; Starren, Justin

2011-01-01

Background Although the penetration of electronic health records is increasing rapidly, much of the historical medical record is only available in handwritten notes and forms, which require labor-intensive, human chart abstraction for some clinical research. The few previous studies on automated extraction of data from these handwritten notes have focused on monolithic, custom-developed recognition systems or third-party systems that require proprietary forms. Methods We present an optical character recognition processing pipeline, which leverages the capabilities of existing third-party optical character recognition engines, and provides the flexibility offered by a modular custom-developed system. The system was configured and run on a selected set of form fields extracted from a corpus of handwritten ophthalmology forms. Observations The processing pipeline allowed multiple configurations to be run, with the optimal configuration consisting of the Nuance and LEADTOOLS engines running in parallel with a positive predictive value of 94.6% and a sensitivity of 13.5%. Discussion While limitations exist, preliminary experience from this project yielded insights on the generalizability and applicability of integrating multiple, inexpensive general-purpose third-party optical character recognition engines in a modular pipeline. PMID:21890871

Unconstrained handwritten numeral recognition based on radial basis competitive and cooperative networks with spatio-temporal feature representation.

PubMed

Lee, S; Pan, J J

1996-01-01

This paper presents a new approach to representation and recognition of handwritten numerals. The approach first transforms a two-dimensional (2-D) spatial representation of a numeral into a three-dimensional (3-D) spatio-temporal representation by identifying the tracing sequence based on a set of heuristic rules acting as transformation operators. A multiresolution critical-point segmentation method is then proposed to extract local feature points, at varying degrees of scale and coarseness. A new neural network architecture, referred to as radial-basis competitive and cooperative network (RCCN), is presented especially for handwritten numeral recognition. RCCN is a globally competitive and locally cooperative network with the capability of self-organizing hidden units to progressively achieve desired network performance, and functions as a universal approximator of arbitrary input-output mappings. Three types of RCCNs are explored: input-space RCCN (IRCCN), output-space RCCN (ORCCN), and bidirectional RCCN (BRCCN). Experiments against handwritten zip code numerals acquired by the U.S. Postal Service indicated that the proposed method is robust in terms of variations, deformations, transformations, and corruption, achieving about 97% recognition rate.

Interpreting Chicken-Scratch: Lexical Access for Handwritten Words

PubMed Central

Barnhart, Anthony S.; Goldinger, Stephen D.

2014-01-01

Handwritten word recognition is a field of study that has largely been neglected in the psychological literature, despite its prevalence in society. Whereas studies of spoken word recognition almost exclusively employ natural, human voices as stimuli, studies of visual word recognition use synthetic typefaces, thus simplifying the process of word recognition. The current study examined the effects of handwriting on a series of lexical variables thought to influence bottom-up and top-down processing, including word frequency, regularity, bidirectional consistency, and imageability. The results suggest that the natural physical ambiguity of handwritten stimuli forces a greater reliance on top-down processes, because almost all effects were magnified, relative to conditions with computer print. These findings suggest that processes of word perception naturally adapt to handwriting, compensating for physical ambiguity by increasing top-down feedback. PMID:20695708

Permutation coding technique for image recognition systems.

PubMed

Kussul, Ernst M; Baidyk, Tatiana N; Wunsch, Donald C; Makeyev, Oleksandr; Martín, Anabel

2006-11-01

A feature extractor and neural classifier for image recognition systems are proposed. The proposed feature extractor is based on the concept of random local descriptors (RLDs). It is followed by the encoder that is based on the permutation coding technique that allows to take into account not only detected features but also the position of each feature on the image and to make the recognition process invariant to small displacements. The combination of RLDs and permutation coding permits us to obtain a sufficiently general description of the image to be recognized. The code generated by the encoder is used as an input data for the neural classifier. Different types of images were used to test the proposed image recognition system. It was tested in the handwritten digit recognition problem, the face recognition problem, and the microobject shape recognition problem. The results of testing are very promising. The error rate for the Modified National Institute of Standards and Technology (MNIST) database is 0.44% and for the Olivetti Research Laboratory (ORL) database it is 0.1%.

Fast Multiclass Segmentation using Diffuse Interface Methods on Graphs

DTIC Science & Technology

2013-02-01

000 28 × 28 images of handwritten digits 0 through 9. Examples of entries can be found in Figure 6. The task is to classify each of the images into the...database of handwritten digits .” [Online]. Available: http://yann.lecun.com/exdb/mnist/ [36] J. Lellmann, J. H. Kappes, J. Yuan, F. Becker, and C...corresponding digit . The images include digits from 0 to 9; thus, this is a 10 class segmentation problem. To construct the weight matrix, we used N

Combination of dynamic Bayesian network classifiers for the recognition of degraded characters

NASA Astrophysics Data System (ADS)

Likforman-Sulem, Laurence; Sigelle, Marc

2009-01-01

We investigate in this paper the combination of DBN (Dynamic Bayesian Network) classifiers, either independent or coupled, for the recognition of degraded characters. The independent classifiers are a vertical HMM and a horizontal HMM whose observable outputs are the image columns and the image rows respectively. The coupled classifiers, presented in a previous study, associate the vertical and horizontal observation streams into single DBNs. The scores of the independent and coupled classifiers are then combined linearly at the decision level. We compare the different classifiers -independent, coupled or linearly combined- on two tasks: the recognition of artificially degraded handwritten digits and the recognition of real degraded old printed characters. Our results show that coupled DBNs perform better on degraded characters than the linear combination of independent HMM scores. Our results also show that the best classifier is obtained by linearly combining the scores of the best coupled DBN and the best independent HMM.

Concurrent evolution of feature extractors and modular artificial neural networks

NASA Astrophysics Data System (ADS)

Hannak, Victor; Savakis, Andreas; Yang, Shanchieh Jay; Anderson, Peter

2009-05-01

This paper presents a new approach for the design of feature-extracting recognition networks that do not require expert knowledge in the application domain. Feature-Extracting Recognition Networks (FERNs) are composed of interconnected functional nodes (feurons), which serve as feature extractors, and are followed by a subnetwork of traditional neural nodes (neurons) that act as classifiers. A concurrent evolutionary process (CEP) is used to search the space of feature extractors and neural networks in order to obtain an optimal recognition network that simultaneously performs feature extraction and recognition. By constraining the hill-climbing search functionality of the CEP on specific parts of the solution space, i.e., individually limiting the evolution of feature extractors and neural networks, it was demonstrated that concurrent evolution is a necessary component of the system. Application of this approach to a handwritten digit recognition task illustrates that the proposed methodology is capable of producing recognition networks that perform in-line with other methods without the need for expert knowledge in image processing.

Evaluating structural pattern recognition for handwritten math via primitive label graphs

NASA Astrophysics Data System (ADS)

Zanibbi, Richard; Mouchère, Harold; Viard-Gaudin, Christian

2013-01-01

Currently, structural pattern recognizer evaluations compare graphs of detected structure to target structures (i.e. ground truth) using recognition rates, recall and precision for object segmentation, classification and relationships. In document recognition, these target objects (e.g. symbols) are frequently comprised of multiple primitives (e.g. connected components, or strokes for online handwritten data), but current metrics do not characterize errors at the primitive level, from which object-level structure is obtained. Primitive label graphs are directed graphs defined over primitives and primitive pairs. We define new metrics obtained by Hamming distances over label graphs, which allow classification, segmentation and parsing errors to be characterized separately, or using a single measure. Recall and precision for detected objects may also be computed directly from label graphs. We illustrate the new metrics by comparing a new primitive-level evaluation to the symbol-level evaluation performed for the CROHME 2012 handwritten math recognition competition. A Python-based set of utilities for evaluating, visualizing and translating label graphs is publicly available.

Optical character recognition with feature extraction and associative memory matrix

NASA Astrophysics Data System (ADS)

Sasaki, Osami; Shibahara, Akihito; Suzuki, Takamasa

1998-06-01

A method is proposed in which handwritten characters are recognized using feature extraction and an associative memory matrix. In feature extraction, simple processes such as shifting and superimposing patterns are executed. A memory matrix is generated with singular value decomposition and by modifying small singular values. The method is optically implemented with two liquid crystal displays. Experimental results for the recognition of 25 handwritten alphabet characters clearly shows the effectiveness of the method.

Combining approaches to on-line handwriting information retrieval

NASA Astrophysics Data System (ADS)

Peña Saldarriaga, Sebastián; Viard-Gaudin, Christian; Morin, Emmanuel

2010-01-01

In this work, we propose to combine two quite different approaches for retrieving handwritten documents. Our hypothesis is that different retrieval algorithms should retrieve different sets of documents for the same query. Therefore, significant improvements in retrieval performances can be expected. The first approach is based on information retrieval techniques carried out on the noisy texts obtained through handwriting recognition, while the second approach is recognition-free using a word spotting algorithm. Results shows that for texts having a word error rate (WER) lower than 23%, the performances obtained with the combined system are close to the performances obtained on clean digital texts. In addition, for poorly recognized texts (WER > 52%), an improvement of nearly 17% can be observed with respect to the best available baseline method.

Diverse spike-timing-dependent plasticity based on multilevel HfO x memristor for neuromorphic computing

NASA Astrophysics Data System (ADS)

Lu, Ke; Li, Yi; He, Wei-Fan; Chen, Jia; Zhou, Ya-Xiong; Duan, Nian; Jin, Miao-Miao; Gu, Wei; Xue, Kan-Hao; Sun, Hua-Jun; Miao, Xiang-Shui

2018-06-01

Memristors have emerged as promising candidates for artificial synaptic devices, serving as the building block of brain-inspired neuromorphic computing. In this letter, we developed a Pt/HfO x /Ti memristor with nonvolatile multilevel resistive switching behaviors due to the evolution of the conductive filaments and the variation in the Schottky barrier. Diverse state-dependent spike-timing-dependent-plasticity (STDP) functions were implemented with different initial resistance states. The measured STDP forms were adopted as the learning rule for a three-layer spiking neural network which achieves a 75.74% recognition accuracy for MNIST handwritten digit dataset. This work has shown the capability of memristive synapse in spiking neural networks for pattern recognition application.

Comparative implementation of Handwritten and Machine written Gurmukhi text utilizing appropriate parameters

NASA Astrophysics Data System (ADS)

Kaur, Jaswinder; Jagdev, Gagandeep, Dr.

2018-01-01

Optical character recognition is concerned with the recognition of optically processed characters. The recognition is done offline after the writing or printing has been completed, unlike online recognition where the computer has to recognize the characters instantly as they are drawn. The performance of character recognition depends upon the quality of scanned documents. The preprocessing steps are used for removing low-frequency background noise and normalizing the intensity of individual scanned documents. Several filters are used for reducing certain image details and enabling an easier or faster evaluation. The primary aim of the research work is to recognize handwritten and machine written characters and differentiate them. The language opted for the research work is Punjabi Gurmukhi and tool utilized is Matlab.

Eye movements when reading sentences with handwritten words.

PubMed

Perea, Manuel; Marcet, Ana; Uixera, Beatriz; Vergara-Martínez, Marta

2016-10-17

The examination of how we read handwritten words (i.e., the original form of writing) has typically been disregarded in the literature on reading. Previous research using word recognition tasks has shown that lexical effects (e.g., the word-frequency effect) are magnified when reading difficult handwritten words. To examine this issue in a more ecological scenario, we registered the participants' eye movements when reading handwritten sentences that varied in the degree of legibility (i.e., sentences composed of words in easy vs. difficult handwritten style). For comparison purposes, we included a condition with printed sentences. Results showed a larger reading cost for sentences with difficult handwritten words than for sentences with easy handwritten words, which in turn showed a reading cost relative to the sentences with printed words. Critically, the effect of word frequency was greater for difficult handwritten words than for easy handwritten words or printed words in the total times on a target word, but not on first-fixation durations or gaze durations. We examine the implications of these findings for models of eye movement control in reading.

A distinguishing method of printed and handwritten legal amount on Chinese bank check

NASA Astrophysics Data System (ADS)

Zhu, Ningbo; Lou, Zhen; Yang, Jingyu

2003-09-01

While carrying out Optical Chinese Character Recognition, distinguishing the font between printed and handwritten characters at the early phase is necessary, because there is so much difference between the methods on recognizing these two types of characters. In this paper, we proposed a good method on how to banish seals and its relative standards that can judge whether they should be banished. Meanwhile, an approach on clearing up scattered noise shivers after image segmentation is presented. Four sets of classifying features that show discrimination between printed and handwritten characters are well adopted. The proposed approach was applied to an automatic check processing system and tested on about 9031 checks. The recognition rate is more than 99.5%.

A perceptive method for handwritten text segmentation

NASA Astrophysics Data System (ADS)

Lemaitre, Aurélie; Camillerapp, Jean; Coüasnon, Bertrand

2011-01-01

This paper presents a new method to address the problem of handwritten text segmentation into text lines and words. Thus, we propose a method based on the cooperation among points of view that enables the localization of the text lines in a low resolution image, and then to associate the pixels at a higher level of resolution. Thanks to the combination of levels of vision, we can detect overlapping characters and re-segment the connected components during the analysis. Then, we propose a segmentation of lines into words based on the cooperation among digital data and symbolic knowledge. The digital data are obtained from distances inside a Delaunay graph, which gives a precise distance between connected components, at the pixel level. We introduce structural rules in order to take into account some generic knowledge about the organization of a text page. This cooperation among information gives a bigger power of expression and ensures the global coherence of the recognition. We validate this work using the metrics and the database proposed for the segmentation contest of ICDAR 2009. Thus, we show that our method obtains very interesting results, compared to the other methods of the literature. More precisely, we are able to deal with slope and curvature, overlapping text lines and varied kinds of writings, which are the main difficulties met by the other methods.

The recognition of graphical patterns invariant to geometrical transformation of the models

NASA Astrophysics Data System (ADS)

Ileană, Ioan; Rotar, Corina; Muntean, Maria; Ceuca, Emilian

2010-11-01

In case that a pattern recognition system is used for images recognition (in robot vision, handwritten recognition etc.), the system must have the capacity to identify an object indifferently of its size or position in the image. The problem of the invariance of recognition can be approached in some fundamental modes. One may apply the similarity criterion used in associative recall. The original pattern is replaced by a mathematical transform that assures some invariance (e.g. the value of two-dimensional Fourier transformation is translation invariant, the value of Mellin transformation is scale invariant). In a different approach the original pattern is represented through a set of features, each of them being coded indifferently of the position, orientation or position of the pattern. Generally speaking, it is easy to obtain invariance in relation with one transformation group, but is difficult to obtain simultaneous invariance at rotation, translation and scale. In this paper we analyze some methods to achieve invariant recognition of images, particularly for digit images. A great number of experiments are due and the conclusions are underplayed in the paper.

Korean letter handwritten recognition using deep convolutional neural network on android platform

NASA Astrophysics Data System (ADS)

Purnamawati, S.; Rachmawati, D.; Lumanauw, G.; Rahmat, R. F.; Taqyuddin, R.

2018-03-01

Currently, popularity of Korean culture attracts many people to learn everything about Korea, particularly its language. To acquire Korean Language, every single learner needs to be able to understand Korean non-Latin character. A digital approach needs to be carried out in order to make Korean learning process easier. This study is done by using Deep Convolutional Neural Network (DCNN). DCNN performs the recognition process on the image based on the model that has been trained such as Inception-v3 Model. Subsequently, re-training process using transfer learning technique with the trained and re-trained value of model is carried though in order to develop a new model with a better performance without any specific systemic errors. The testing accuracy of this research results in 86,9%.

HMM-based lexicon-driven and lexicon-free word recognition for online handwritten Indic scripts.

PubMed

Bharath, A; Madhvanath, Sriganesh

2012-04-01

Research for recognizing online handwritten words in Indic scripts is at its early stages when compared to Latin and Oriental scripts. In this paper, we address this problem specifically for two major Indic scripts--Devanagari and Tamil. In contrast to previous approaches, the techniques we propose are largely data driven and script independent. We propose two different techniques for word recognition based on Hidden Markov Models (HMM): lexicon driven and lexicon free. The lexicon-driven technique models each word in the lexicon as a sequence of symbol HMMs according to a standard symbol writing order derived from the phonetic representation. The lexicon-free technique uses a novel Bag-of-Symbols representation of the handwritten word that is independent of symbol order and allows rapid pruning of the lexicon. On handwritten Devanagari word samples featuring both standard and nonstandard symbol writing orders, a combination of lexicon-driven and lexicon-free recognizers significantly outperforms either of them used in isolation. In contrast, most Tamil word samples feature the standard symbol order, and the lexicon-driven recognizer outperforms the lexicon free one as well as their combination. The best recognition accuracies obtained for 20,000 word lexicons are 87.13 percent for Devanagari when the two recognizers are combined, and 91.8 percent for Tamil using the lexicon-driven technique.

Neural Networks for Handwritten English Alphabet Recognition

NASA Astrophysics Data System (ADS)

Perwej, Yusuf; Chaturvedi, Ashish

2011-04-01

This paper demonstrates the use of neural networks for developing a system that can recognize hand-written English alphabets. In this system, each English alphabet is represented by binary values that are used as input to a simple feature extraction system, whose output is fed to our neural network system.

Character context: a shape descriptor for Arabic handwriting recognition

NASA Astrophysics Data System (ADS)

Mudhsh, Mohammed; Almodfer, Rolla; Duan, Pengfei; Xiong, Shengwu

2017-11-01

In the handwriting recognition field, designing good descriptors are substantial to obtain rich information of the data. However, the handwriting recognition research of a good descriptor is still an open issue due to unlimited variation in human handwriting. We introduce a "character context descriptor" that efficiently dealt with the structural characteristics of Arabic handwritten characters. First, the character image is smoothed and normalized, then the character context descriptor of 32 feature bins is built based on the proposed "distance function." Finally, a multilayer perceptron with regularization is used as a classifier. On experimentation with a handwritten Arabic characters database, the proposed method achieved a state-of-the-art performance with recognition rate equal to 98.93% and 99.06% for the 66 and 24 classes, respectively.

Online handwritten mathematical expression recognition

NASA Astrophysics Data System (ADS)

Büyükbayrak, Hakan; Yanikoglu, Berrin; Erçil, Aytül

2007-01-01

We describe a system for recognizing online, handwritten mathematical expressions. The system is designed with a user-interface for writing scientific articles, supporting the recognition of basic mathematical expressions as well as integrals, summations, matrices etc. A feed-forward neural network recognizes symbols which are assumed to be single-stroke and a recursive algorithm parses the expression by combining neural network output and the structure of the expression. Preliminary results show that writer-dependent recognition rates are very high (99.8%) while writer-independent symbol recognition rates are lower (75%). The interface associated with the proposed system integrates the built-in recognition capabilities of the Microsoft's Tablet PC API for recognizing textual input and supports conversion of hand-drawn figures into PNG format. This enables the user to enter text, mathematics and draw figures in a single interface. After recognition, all output is combined into one LATEX code and compiled into a PDF file.

Deformation-Aware Log-Linear Models

NASA Astrophysics Data System (ADS)

Gass, Tobias; Deselaers, Thomas; Ney, Hermann

In this paper, we present a novel deformation-aware discriminative model for handwritten digit recognition. Unlike previous approaches our model directly considers image deformations and allows discriminative training of all parameters, including those accounting for non-linear transformations of the image. This is achieved by extending a log-linear framework to incorporate a latent deformation variable. The resulting model has an order of magnitude less parameters than competing approaches to handling image deformations. We tune and evaluate our approach on the USPS task and show its generalization capabilities by applying the tuned model to the MNIST task. We gain interesting insights and achieve highly competitive results on both tasks.

Identification of handwriting by using the genetic algorithm (GA) and support vector machine (SVM)

NASA Astrophysics Data System (ADS)

Zhang, Qigui; Deng, Kai

2016-12-01

As portable digital camera and a camera phone comes more and more popular, and equally pressing is meeting the requirements of people to shoot at any time, to identify and storage handwritten character. In this paper, genetic algorithm(GA) and support vector machine(SVM)are used for identification of handwriting. Compare with parameters-optimized method, this technique overcomes two defects: first, it's easy to trap in the local optimum; second, finding the best parameters in the larger range will affects the efficiency of classification and prediction. As the experimental results suggest, GA-SVM has a higher recognition rate.

Recognition of Handwritten Arabic words using a neuro-fuzzy network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boukharouba, Abdelhak; Bennia, Abdelhak

We present a new method for the recognition of handwritten Arabic words based on neuro-fuzzy hybrid network. As a first step, connected components (CCs) of black pixels are detected. Then the system determines which CCs are sub-words and which are stress marks. The stress marks are then isolated and identified separately and the sub-words are segmented into graphemes. Each grapheme is described by topological and statistical features. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data using a fuzzy c-means, and rule parameter tuning phase using gradient descentmore » learning. After learning, the network encodes in its topology the essential design parameters of a fuzzy inference system.The contribution of this technique is shown through the significant tests performed on a handwritten Arabic words database.« less

Iterative cross section sequence graph for handwritten character segmentation.

PubMed

Dawoud, Amer

2007-08-01

The iterative cross section sequence graph (ICSSG) is an algorithm for handwritten character segmentation. It expands the cross section sequence graph concept by applying it iteratively at equally spaced thresholds. The iterative thresholding reduces the effect of information loss associated with image binarization. ICSSG preserves the characters' skeletal structure by preventing the interference of pixels that causes flooding of adjacent characters' segments. Improving the structural quality of the characters' skeleton facilitates better feature extraction and classification, which improves the overall performance of optical character recognition (OCR). Experimental results showed significant improvements in OCR recognition rates compared to other well-established segmentation algorithms.

Determining the Value of Handwritten Comments within Work Orders

ERIC Educational Resources Information Center

Thombs, Daniel

2010-01-01

In the workplace many work orders are handwritten on paper rather than recorded in a digital format. Despite being archived, these documents are neither referenced nor analyzed after their creation. Tacit knowledge gathered though employee documentation is generally considered beneficial, but only if it can be easily gathered and processed. …

Kernel-aligned multi-view canonical correlation analysis for image recognition

NASA Astrophysics Data System (ADS)

Su, Shuzhi; Ge, Hongwei; Yuan, Yun-Hao

2016-09-01

Existing kernel-based correlation analysis methods mainly adopt a single kernel in each view. However, only a single kernel is usually insufficient to characterize nonlinear distribution information of a view. To solve the problem, we transform each original feature vector into a 2-dimensional feature matrix by means of kernel alignment, and then propose a novel kernel-aligned multi-view canonical correlation analysis (KAMCCA) method on the basis of the feature matrices. Our proposed method can simultaneously employ multiple kernels to better capture the nonlinear distribution information of each view, so that correlation features learned by KAMCCA can have well discriminating power in real-world image recognition. Extensive experiments are designed on five real-world image datasets, including NIR face images, thermal face images, visible face images, handwritten digit images, and object images. Promising experimental results on the datasets have manifested the effectiveness of our proposed method.

Dynamic and Contextual Information in HMM Modeling for Handwritten Word Recognition.

PubMed

Bianne-Bernard, Anne-Laure; Menasri, Farès; Al-Hajj Mohamad, Rami; Mokbel, Chafic; Kermorvant, Christopher; Likforman-Sulem, Laurence

2011-10-01

This study aims at building an efficient word recognition system resulting from the combination of three handwriting recognizers. The main component of this combined system is an HMM-based recognizer which considers dynamic and contextual information for a better modeling of writing units. For modeling the contextual units, a state-tying process based on decision tree clustering is introduced. Decision trees are built according to a set of expert-based questions on how characters are written. Questions are divided into global questions, yielding larger clusters, and precise questions, yielding smaller ones. Such clustering enables us to reduce the total number of models and Gaussians densities by 10. We then apply this modeling to the recognition of handwritten words. Experiments are conducted on three publicly available databases based on Latin or Arabic languages: Rimes, IAM, and OpenHart. The results obtained show that contextual information embedded with dynamic modeling significantly improves recognition.

Recognition of handprinted characters for automated cartography A progress report

NASA Technical Reports Server (NTRS)

Lybanon, M.; Brown, R. M.; Gronmeyer, L. K.

1980-01-01

A research program for developing handwritten character recognition techniques is reported. The generation of cartographic/hydrographic manuscripts is overviewed. The performance of hardware/software systems is discussed, along with future research problem areas and planned approaches.

Dual function seal: visualized digital signature for electronic medical record systems.

PubMed

Yu, Yao-Chang; Hou, Ting-Wei; Chiang, Tzu-Chiang

2012-10-01

Digital signature is an important cryptography technology to be used to provide integrity and non-repudiation in electronic medical record systems (EMRS) and it is required by law. However, digital signatures normally appear in forms unrecognizable to medical staff, this may reduce the trust from medical staff that is used to the handwritten signatures or seals. Therefore, in this paper we propose a dual function seal to extend user trust from a traditional seal to a digital signature. The proposed dual function seal is a prototype that combines the traditional seal and digital seal. With this prototype, medical personnel are not just can put a seal on paper but also generate a visualized digital signature for electronic medical records. Medical Personnel can then look at the visualized digital signature and directly know which medical personnel generated it, just like with a traditional seal. Discrete wavelet transform (DWT) is used as an image processing method to generate a visualized digital signature, and the peak signal to noise ratio (PSNR) is calculated to verify that distortions of all converted images are beyond human recognition, and the results of our converted images are from 70 dB to 80 dB. The signature recoverability is also tested in this proposed paper to ensure that the visualized digital signature is verifiable. A simulated EMRS is implemented to show how the visualized digital signature can be integrity into EMRS.

Real-time classification and sensor fusion with a spiking deep belief network.

PubMed

O'Connor, Peter; Neil, Daniel; Liu, Shih-Chii; Delbruck, Tobi; Pfeiffer, Michael

2013-01-01

Deep Belief Networks (DBNs) have recently shown impressive performance on a broad range of classification problems. Their generative properties allow better understanding of the performance, and provide a simpler solution for sensor fusion tasks. However, because of their inherent need for feedback and parallel update of large numbers of units, DBNs are expensive to implement on serial computers. This paper proposes a method based on the Siegert approximation for Integrate-and-Fire neurons to map an offline-trained DBN onto an efficient event-driven spiking neural network suitable for hardware implementation. The method is demonstrated in simulation and by a real-time implementation of a 3-layer network with 2694 neurons used for visual classification of MNIST handwritten digits with input from a 128 × 128 Dynamic Vision Sensor (DVS) silicon retina, and sensory-fusion using additional input from a 64-channel AER-EAR silicon cochlea. The system is implemented through the open-source software in the jAER project and runs in real-time on a laptop computer. It is demonstrated that the system can recognize digits in the presence of distractions, noise, scaling, translation and rotation, and that the degradation of recognition performance by using an event-based approach is less than 1%. Recognition is achieved in an average of 5.8 ms after the onset of the presentation of a digit. By cue integration from both silicon retina and cochlea outputs we show that the system can be biased to select the correct digit from otherwise ambiguous input.

A comparison of 1D and 2D LSTM architectures for the recognition of handwritten Arabic

NASA Astrophysics Data System (ADS)

Yousefi, Mohammad Reza; Soheili, Mohammad Reza; Breuel, Thomas M.; Stricker, Didier

2015-01-01

In this paper, we present an Arabic handwriting recognition method based on recurrent neural network. We use the Long Short Term Memory (LSTM) architecture, that have proven successful in different printed and handwritten OCR tasks. Applications of LSTM for handwriting recognition employ the two-dimensional architecture to deal with the variations in both vertical and horizontal axis. However, we show that using a simple pre-processing step that normalizes the position and baseline of letters, we can make use of 1D LSTM, which is faster in learning and convergence, and yet achieve superior performance. In a series of experiments on IFN/ENIT database for Arabic handwriting recognition, we demonstrate that our proposed pipeline can outperform 2D LSTM networks. Furthermore, we provide comparisons with 1D LSTM networks trained with manually crafted features to show that the automatically learned features in a globally trained 1D LSTM network with our normalization step can even outperform such systems.

Handwritten mathematical symbols dataset.

PubMed

Chajri, Yassine; Bouikhalene, Belaid

2016-06-01

Due to the technological advances in recent years, paper scientific documents are used less and less. Thus, the trend in the scientific community to use digital documents has increased considerably. Among these documents, there are scientific documents and more specifically mathematics documents. In this context, we present our own dataset of handwritten mathematical symbols composed of 10,379 images. This dataset gathers Arabic characters, Latin characters, Arabic numerals, Latin numerals, arithmetic operators, set-symbols, comparison symbols, delimiters, etc.

Semi-automatic ground truth generation using unsupervised clustering and limited manual labeling: Application to handwritten character recognition

PubMed Central

Vajda, Szilárd; Rangoni, Yves; Cecotti, Hubert

2015-01-01

For training supervised classifiers to recognize different patterns, large data collections with accurate labels are necessary. In this paper, we propose a generic, semi-automatic labeling technique for large handwritten character collections. In order to speed up the creation of a large scale ground truth, the method combines unsupervised clustering and minimal expert knowledge. To exploit the potential discriminant complementarities across features, each character is projected into five different feature spaces. After clustering the images in each feature space, the human expert labels the cluster centers. Each data point inherits the label of its cluster’s center. A majority (or unanimity) vote decides the label of each character image. The amount of human involvement (labeling) is strictly controlled by the number of clusters – produced by the chosen clustering approach. To test the efficiency of the proposed approach, we have compared, and evaluated three state-of-the art clustering methods (k-means, self-organizing maps, and growing neural gas) on the MNIST digit data set, and a Lampung Indonesian character data set, respectively. Considering a k-nn classifier, we show that labeling manually only 1.3% (MNIST), and 3.2% (Lampung) of the training data, provides the same range of performance than a completely labeled data set would. PMID:25870463

Structural model constructing for optical handwritten character recognition

NASA Astrophysics Data System (ADS)

Khaustov, P. A.; Spitsyn, V. G.; Maksimova, E. I.

2017-02-01

The article is devoted to the development of the algorithms for optical handwritten character recognition based on the structural models constructing. The main advantage of these algorithms is the low requirement regarding the number of reference images. The one-pass approach to a thinning of the binary character representation has been proposed. This approach is based on the joint use of Zhang-Suen and Wu-Tsai algorithms. The effectiveness of the proposed approach is confirmed by the results of the experiments. The article includes the detailed description of the structural model constructing algorithm’s steps. The proposed algorithm has been implemented in character processing application and has been approved on MNIST handwriting characters database. Algorithms that could be used in case of limited reference images number were used for the comparison.

An online handwriting recognition system for Turkish

NASA Astrophysics Data System (ADS)

Vural, Esra; Erdogan, Hakan; Oflazer, Kemal; Yanikoglu, Berrin A.

2004-12-01

Despite recent developments in Tablet PC technology, there has not been any applications for recognizing handwritings in Turkish. In this paper, we present an online handwritten text recognition system for Turkish, developed using the Tablet PC interface. However, even though the system is developed for Turkish, the addressed issues are common to online handwriting recognition systems in general. Several dynamic features are extracted from the handwriting data for each recorded point and Hidden Markov Models (HMM) are used to train letter and word models. We experimented with using various features and HMM model topologies, and report on the effects of these experiments. We started with first and second derivatives of the x and y coordinates and relative change in the pen pressure as initial features. We found that using two more additional features, that is, number of neighboring points and relative heights of each point with respect to the base-line improve the recognition rate. In addition, extracting features within strokes and using a skipping state topology improve the system performance as well. The improved system performance is 94% in recognizing handwritten words from a 1000-word lexicon.

An online handwriting recognition system for Turkish

NASA Astrophysics Data System (ADS)

Vural, Esra; Erdogan, Hakan; Oflazer, Kemal; Yanikoglu, Berrin A.

2005-01-01

Despite recent developments in Tablet PC technology, there has not been any applications for recognizing handwritings in Turkish. In this paper, we present an online handwritten text recognition system for Turkish, developed using the Tablet PC interface. However, even though the system is developed for Turkish, the addressed issues are common to online handwriting recognition systems in general. Several dynamic features are extracted from the handwriting data for each recorded point and Hidden Markov Models (HMM) are used to train letter and word models. We experimented with using various features and HMM model topologies, and report on the effects of these experiments. We started with first and second derivatives of the x and y coordinates and relative change in the pen pressure as initial features. We found that using two more additional features, that is, number of neighboring points and relative heights of each point with respect to the base-line improve the recognition rate. In addition, extracting features within strokes and using a skipping state topology improve the system performance as well. The improved system performance is 94% in recognizing handwritten words from a 1000-word lexicon.

Offline handwritten word recognition using MQDF-HMMs

NASA Astrophysics Data System (ADS)

Ramachandrula, Sitaram; Hambarde, Mangesh; Patial, Ajay; Sahoo, Dushyant; Kochar, Shaivi

2015-01-01

We propose an improved HMM formulation for offline handwriting recognition (HWR). The main contribution of this work is using modified quadratic discriminant function (MQDF) [1] within HMM framework. In an MQDF-HMM the state observation likelihood is calculated by a weighted combination of MQDF likelihoods of individual Gaussians of GMM (Gaussian Mixture Model). The quadratic discriminant function (QDF) of a multivariate Gaussian can be rewritten by avoiding the inverse of covariance matrix by using the Eigen values and Eigen vectors of it. The MQDF is derived from QDF by substituting few of badly estimated lower-most Eigen values by an appropriate constant. The estimation errors of non-dominant Eigen vectors and Eigen values of covariance matrix for which the training data is insufficient can be controlled by this approach. MQDF has been successfully shown to improve the character recognition performance [1]. The usage of MQDF in HMM improves the computation, storage and modeling power of HMM when there is limited training data. We have got encouraging results on offline handwritten character (NIST database) and word recognition in English using MQDF HMMs.

Real-time classification and sensor fusion with a spiking deep belief network

PubMed Central

O'Connor, Peter; Neil, Daniel; Liu, Shih-Chii; Delbruck, Tobi; Pfeiffer, Michael

2013-01-01

Deep Belief Networks (DBNs) have recently shown impressive performance on a broad range of classification problems. Their generative properties allow better understanding of the performance, and provide a simpler solution for sensor fusion tasks. However, because of their inherent need for feedback and parallel update of large numbers of units, DBNs are expensive to implement on serial computers. This paper proposes a method based on the Siegert approximation for Integrate-and-Fire neurons to map an offline-trained DBN onto an efficient event-driven spiking neural network suitable for hardware implementation. The method is demonstrated in simulation and by a real-time implementation of a 3-layer network with 2694 neurons used for visual classification of MNIST handwritten digits with input from a 128 × 128 Dynamic Vision Sensor (DVS) silicon retina, and sensory-fusion using additional input from a 64-channel AER-EAR silicon cochlea. The system is implemented through the open-source software in the jAER project and runs in real-time on a laptop computer. It is demonstrated that the system can recognize digits in the presence of distractions, noise, scaling, translation and rotation, and that the degradation of recognition performance by using an event-based approach is less than 1%. Recognition is achieved in an average of 5.8 ms after the onset of the presentation of a digit. By cue integration from both silicon retina and cochlea outputs we show that the system can be biased to select the correct digit from otherwise ambiguous input. PMID:24115919

A Parallel Neuromorphic Text Recognition System and Its Implementation on a Heterogeneous High-Performance Computing Cluster

DTIC Science & Technology

2013-01-01

M. Ahmadi, and M. Shridhar, “ Handwritten Numeral Recognition with Multiple Features and Multistage Classifiers,” Proc. IEEE Int’l Symp. Circuits...ARTICLE (Post Print) 3. DATES COVERED (From - To) SEP 2011 – SEP 2013 4. TITLE AND SUBTITLE A PARALLEL NEUROMORPHIC TEXT RECOGNITION SYSTEM AND ITS...research in computational intelligence has entered a new era. In this paper, we present an HPC-based context-aware intelligent text recognition

Optical character recognition of handwritten Arabic using hidden Markov models

NASA Astrophysics Data System (ADS)

Aulama, Mohannad M.; Natsheh, Asem M.; Abandah, Gheith A.; Olama, Mohammed M.

2011-04-01

The problem of optical character recognition (OCR) of handwritten Arabic has not received a satisfactory solution yet. In this paper, an Arabic OCR algorithm is developed based on Hidden Markov Models (HMMs) combined with the Viterbi algorithm, which results in an improved and more robust recognition of characters at the sub-word level. Integrating the HMMs represents another step of the overall OCR trends being currently researched in the literature. The proposed approach exploits the structure of characters in the Arabic language in addition to their extracted features to achieve improved recognition rates. Useful statistical information of the Arabic language is initially extracted and then used to estimate the probabilistic parameters of the mathematical HMM. A new custom implementation of the HMM is developed in this study, where the transition matrix is built based on the collected large corpus, and the emission matrix is built based on the results obtained via the extracted character features. The recognition process is triggered using the Viterbi algorithm which employs the most probable sequence of sub-words. The model was implemented to recognize the sub-word unit of Arabic text raising the recognition rate from being linked to the worst recognition rate for any character to the overall structure of the Arabic language. Numerical results show that there is a potentially large recognition improvement by using the proposed algorithms.

Optical character recognition of handwritten Arabic using hidden Markov models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aulama, Mohannad M.; Natsheh, Asem M.; Abandah, Gheith A.

2011-01-01

The problem of optical character recognition (OCR) of handwritten Arabic has not received a satisfactory solution yet. In this paper, an Arabic OCR algorithm is developed based on Hidden Markov Models (HMMs) combined with the Viterbi algorithm, which results in an improved and more robust recognition of characters at the sub-word level. Integrating the HMMs represents another step of the overall OCR trends being currently researched in the literature. The proposed approach exploits the structure of characters in the Arabic language in addition to their extracted features to achieve improved recognition rates. Useful statistical information of the Arabic language ismore » initially extracted and then used to estimate the probabilistic parameters of the mathematical HMM. A new custom implementation of the HMM is developed in this study, where the transition matrix is built based on the collected large corpus, and the emission matrix is built based on the results obtained via the extracted character features. The recognition process is triggered using the Viterbi algorithm which employs the most probable sequence of sub-words. The model was implemented to recognize the sub-word unit of Arabic text raising the recognition rate from being linked to the worst recognition rate for any character to the overall structure of the Arabic language. Numerical results show that there is a potentially large recognition improvement by using the proposed algorithms.« less

Handwritten mathematical symbols dataset

PubMed Central

Chajri, Yassine; Bouikhalene, Belaid

2016-01-01

Due to the technological advances in recent years, paper scientific documents are used less and less. Thus, the trend in the scientific community to use digital documents has increased considerably. Among these documents, there are scientific documents and more specifically mathematics documents. In this context, we present our own dataset of handwritten mathematical symbols composed of 10,379 images. This dataset gathers Arabic characters, Latin characters, Arabic numerals, Latin numerals, arithmetic operators, set-symbols, comparison symbols, delimiters, etc. PMID:27006975

Structural analysis of online handwritten mathematical symbols based on support vector machines

NASA Astrophysics Data System (ADS)

Simistira, Foteini; Papavassiliou, Vassilis; Katsouros, Vassilis; Carayannis, George

2013-01-01

Mathematical expression recognition is still a very challenging task for the research community mainly because of the two-dimensional (2d) structure of mathematical expressions (MEs). In this paper, we present a novel approach for the structural analysis between two on-line handwritten mathematical symbols of a ME, based on spatial features of the symbols. We introduce six features to represent the spatial affinity of the symbols and compare two multi-class classification methods that employ support vector machines (SVMs): one based on the "one-against-one" technique and one based on the "one-against-all", in identifying the relation between a pair of symbols (i.e. subscript, numerator, etc). A dataset containing 1906 spatial relations derived from the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) 2012 training dataset is constructed to evaluate the classifiers and compare them with the rule-based classifier of the ILSP-1 system participated in the contest. The experimental results give an overall mean error rate of 2.61% for the "one-against-one" SVM approach, 6.57% for the "one-against-all" SVM technique and 12.31% error rate for the ILSP-1 classifier.

Slant correction for handwritten English documents

NASA Astrophysics Data System (ADS)

Shridhar, Malayappan; Kimura, Fumitaka; Ding, Yimei; Miller, John W. V.

2004-12-01

Optical character recognition of machine-printed documents is an effective means for extracting textural material. While the level of effectiveness for handwritten documents is much poorer, progress is being made in more constrained applications such as personal checks and postal addresses. In these applications a series of steps is performed for recognition beginning with removal of skew and slant. Slant is a characteristic unique to the writer and varies from writer to writer in which characters are tilted some amount from vertical. The second attribute is the skew that arises from the inability of the writer to write on a horizontal line. Several methods have been proposed and discussed for average slant estimation and correction in the earlier papers. However, analysis of many handwritten documents reveals that slant is a local property and slant varies even within a word. The use of an average slant for the entire word often results in overestimation or underestimation of the local slant. This paper describes three methods for local slant estimation, namely the simple iterative method, high-speed iterative method, and the 8-directional chain code method. The experimental results show that the proposed methods can estimate and correct local slant more effectively than the average slant correction.

Analog design of a new neural network for optical character recognition.

PubMed

Morns, I P; Dlay, S S

1999-01-01

An electronic circuit is presented for a new type of neural network, which gives a recognition rate of over 100 kHz. The network is used to classify handwritten numerals, presented as Fourier and wavelet descriptors, and has been shown to train far quicker than the popular backpropagation network while maintaining classification accuracy.

Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the ‘Extreme Learning Machine’ Algorithm

PubMed Central

McDonnell, Mark D.; Tissera, Migel D.; Vladusich, Tony; van Schaik, André; Tapson, Jonathan

2015-01-01

Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolutional neural networks. This is achieved by training such networks using the ‘Extreme Learning Machine’ (ELM) approach, which also enables a very rapid training time (∼ 10 minutes). Adding distortions, as is common practise for MNIST, reduces error rates even further. Our methods are also shown to be capable of achieving less than 5.5% error rates on the NORB image database. To achieve these results, we introduce several enhancements to the standard ELM algorithm, which individually and in combination can significantly improve performance. The main innovation is to ensure each hidden-unit operates only on a randomly sized and positioned patch of each image. This form of random ‘receptive field’ sampling of the input ensures the input weight matrix is sparse, with about 90% of weights equal to zero. Furthermore, combining our methods with a small number of iterations of a single-batch backpropagation method can significantly reduce the number of hidden-units required to achieve a particular performance. Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems. PMID:26262687

On the Optimum Architecture of the Biologically Inspired Hierarchical Temporal Memory Model Applied to the Hand-Written Digit Recognition

NASA Astrophysics Data System (ADS)

Štolc, Svorad; Bajla, Ivan

2010-01-01

In the paper we describe basic functions of the Hierarchical Temporal Memory (HTM) network based on a novel biologically inspired model of the large-scale structure of the mammalian neocortex. The focus of this paper is in a systematic exploration of possibilities how to optimize important controlling parameters of the HTM model applied to the classification of hand-written digits from the USPS database. The statistical properties of this database are analyzed using the permutation test which employs a randomization distribution of the training and testing data. Based on a notion of the homogeneous usage of input image pixels, a methodology of the HTM parameter optimization is proposed. In order to study effects of two substantial parameters of the architecture: the patch size and the overlap in more details, we have restricted ourselves to the single-level HTM networks. A novel method for construction of the training sequences by ordering series of the static images is developed. A novel method for estimation of the parameter maxDist based on the box counting method is proposed. The parameter sigma of the inference Gaussian is optimized on the basis of the maximization of the belief distribution entropy. Both optimization algorithms can be equally applied to the multi-level HTM networks as well. The influences of the parameters transitionMemory and requestedGroupCount on the HTM network performance have been explored. Altogether, we have investigated 2736 different HTM network configurations. The obtained classification accuracy results have been benchmarked with the published results of several conventional classifiers.

House officer procedure documentation using a personal digital assistant: a longitudinal study

PubMed Central

Bird, Steven B; Lane, David R

2006-01-01

Background Personal Digital Assistants (PDAs) have been integrated into daily practice for many emergency physicians and house officers. Few objective data exist that quantify the effect of PDAs on documentation. The objective of this study was to determine whether use of a PDA would improve emergency medicine house officer documentation of procedures and patient resuscitations. Methods Twelve first-year Emergency Medicine (EM) residents were provided a Palm V (Palm, Inc., Santa Clara, California, USA) PDA. A customizable patient procedure and encounter program was constructed and loaded into each PDA. Residents were instructed to enter information on patients who had any of 20 procedures performed, were deemed clinically unstable, or on whom follow-up was obtained. These data were downloaded to the residency coordinator's desktop computer on a weekly basis for 36 months. The mean number of procedures and encounters performed per resident over a three year period were then compared with those of 12 historical controls from a previous residency class that had recorded the same information using a handwritten card system for 36 months. Means of both groups were compared a two-tailed Student's t test with a Bonferroni correction for multiple comparisons. One hundred randomly selected entries from both the PDA and handwritten groups were reviewed for completeness. Another group of 11 residents who had used both handwritten and PDA procedure logs for one year each were asked to complete a questionnaire regarding their satisfaction with the PDA system. Results Mean documentation of three procedures significantly increased in the PDA vs handwritten groups: conscious sedation 24.0 vs 0.03 (p = 0.001); thoracentesis 3.0 vs 0.0 (p = 0.001); and ED ultrasound 24.5 vs. 0.0 (p = 0.001). In the handwritten cohort, only the number of cardioversions/defibrillations (26.5 vs 11.5) was statistically increased (p = 0.001). Of the PDA entries, 100% were entered completely, compared to only 91% of the handwritten group, including 4% that were illegible. 10 of 11 questioned residents preferred the PDA procedure log to a handwritten log (mean ± SD Likert-scale score of 1.6 ± 0.9). Conclusion Overall use of a PDA did not significantly change EM resident procedure or patient resuscitation documentation when used over a three-year period. Statistically significant differences between the handwritten and PDA groups likely represent alterations in the standard of ED care over time. Residents overwhelmingly preferred the PDA procedure log to a handwritten log and more entries are complete using the PDA. These favorable comparisons and the numerous other uses of PDAs may make them an attractive alternative for resident documentation. PMID:16438709

Synthesis of Common Arabic Handwritings to Aid Optical Character Recognition Research.

PubMed

Dinges, Laslo; Al-Hamadi, Ayoub; Elzobi, Moftah; El-Etriby, Sherif

2016-03-11

Document analysis tasks such as pattern recognition, word spotting or segmentation, require comprehensive databases for training and validation. Not only variations in writing style but also the used list of words is of importance in the case that training samples should reflect the input of a specific area of application. However, generation of training samples is expensive in the sense of manpower and time, particularly if complete text pages including complex ground truth are required. This is why there is a lack of such databases, especially for Arabic, the second most popular language. However, Arabic handwriting recognition involves different preprocessing, segmentation and recognition methods. Each requires particular ground truth or samples to enable optimal training and validation, which are often not covered by the currently available databases. To overcome this issue, we propose a system that synthesizes Arabic handwritten words and text pages and generates corresponding detailed ground truth. We use these syntheses to validate a new, segmentation based system that recognizes handwritten Arabic words. We found that a modification of an Active Shape Model based character classifiers-that we proposed earlier-improves the word recognition accuracy. Further improvements are achieved, by using a vocabulary of the 50,000 most common Arabic words for error correction.

Synthesis of Common Arabic Handwritings to Aid Optical Character Recognition Research

PubMed Central

Dinges, Laslo; Al-Hamadi, Ayoub; Elzobi, Moftah; El-etriby, Sherif

2016-01-01

Document analysis tasks such as pattern recognition, word spotting or segmentation, require comprehensive databases for training and validation. Not only variations in writing style but also the used list of words is of importance in the case that training samples should reflect the input of a specific area of application. However, generation of training samples is expensive in the sense of manpower and time, particularly if complete text pages including complex ground truth are required. This is why there is a lack of such databases, especially for Arabic, the second most popular language. However, Arabic handwriting recognition involves different preprocessing, segmentation and recognition methods. Each requires particular ground truth or samples to enable optimal training and validation, which are often not covered by the currently available databases. To overcome this issue, we propose a system that synthesizes Arabic handwritten words and text pages and generates corresponding detailed ground truth. We use these syntheses to validate a new, segmentation based system that recognizes handwritten Arabic words. We found that a modification of an Active Shape Model based character classifiers—that we proposed earlier—improves the word recognition accuracy. Further improvements are achieved, by using a vocabulary of the 50,000 most common Arabic words for error correction. PMID:26978368

Ultrafast learning in a hard-limited neural network pattern recognizer

NASA Astrophysics Data System (ADS)

Hu, Chia-Lun J.

1996-03-01

As we published in the last five years, the supervised learning in a hard-limited perceptron system can be accomplished in a noniterative manner if the input-output mapping to be learned satisfies a certain positive-linear-independency (or PLI) condition. When this condition is satisfied (for most practical pattern recognition applications, this condition should be satisfied,) the connection matrix required to meet this mapping can be obtained noniteratively in one step. Generally, there exist infinitively many solutions for the connection matrix when the PLI condition is satisfied. We can then select an optimum solution such that the recognition of any untrained patterns will become optimally robust in the recognition mode. The learning speed is very fast and close to real-time because the learning process is noniterative and one-step. This paper reports the theoretical analysis and the design of a practical charter recognition system for recognizing hand-written alphabets. The experimental result is recorded in real-time on an unedited video tape for demonstration purposes. It is seen from this real-time movie that the recognition of the untrained hand-written alphabets is invariant to size, location, orientation, and writing sequence, even the training is done with standard size, standard orientation, central location and standard writing sequence.

Reduction of the dimension of neural network models in problems of pattern recognition and forecasting

NASA Astrophysics Data System (ADS)

Nasertdinova, A. D.; Bochkarev, V. V.

2017-11-01

Deep neural networks with a large number of parameters are a powerful tool for solving problems of pattern recognition, prediction and classification. Nevertheless, overfitting remains a serious problem in the use of such networks. A method of solving the problem of overfitting is proposed in this article. This method is based on reducing the number of independent parameters of a neural network model using the principal component analysis, and can be implemented using existing libraries of neural computing. The algorithm was tested on the problem of recognition of handwritten symbols from the MNIST database, as well as on the task of predicting time series (rows of the average monthly number of sunspots and series of the Lorentz system were used). It is shown that the application of the principal component analysis enables reducing the number of parameters of the neural network model when the results are good. The average error rate for the recognition of handwritten figures from the MNIST database was 1.12% (which is comparable to the results obtained using the "Deep training" methods), while the number of parameters of the neural network can be reduced to 130 times.

Feature extraction with deep neural networks by a generalized discriminant analysis.

PubMed

Stuhlsatz, André; Lippel, Jens; Zielke, Thomas

2012-04-01

We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used.

Experiments on Urdu Text Recognition

NASA Astrophysics Data System (ADS)

Mukhtar, Omar; Setlur, Srirangaraj; Govindaraju, Venu

Urdu is a language spoken in the Indian subcontinent by an estimated 130-270 million speakers. At the spoken level, Urdu and Hindi are considered dialects of a single language because of shared vocabulary and the similarity in grammar. At the written level, however, Urdu is much closer to Arabic because it is written in Nastaliq, the calligraphic style of the Persian-Arabic script. Therefore, a speaker of Hindi can understand spoken Urdu but may not be able to read written Urdu because Hindi is written in Devanagari script, whereas an Arabic writer can read the written words but may not understand the spoken Urdu. In this chapter we present an overview of written Urdu. Prior research in handwritten Urdu OCR is very limited. We present (perhaps) the first system for recognizing handwritten Urdu words. On a data set of about 1300 handwritten words, we achieved an accuracy of 70% for the top choice, and 82% for the top three choices.

A comparison study between MLP and convolutional neural network models for character recognition

NASA Astrophysics Data System (ADS)

Ben Driss, S.; Soua, M.; Kachouri, R.; Akil, M.

2017-05-01

Optical Character Recognition (OCR) systems have been designed to operate on text contained in scanned documents and images. They include text detection and character recognition in which characters are described then classified. In the classification step, characters are identified according to their features or template descriptions. Then, a given classifier is employed to identify characters. In this context, we have proposed the unified character descriptor (UCD) to represent characters based on their features. Then, matching was employed to ensure the classification. This recognition scheme performs a good OCR Accuracy on homogeneous scanned documents, however it cannot discriminate characters with high font variation and distortion.3 To improve recognition, classifiers based on neural networks can be used. The multilayer perceptron (MLP) ensures high recognition accuracy when performing a robust training. Moreover, the convolutional neural network (CNN), is gaining nowadays a lot of popularity for its high performance. Furthermore, both CNN and MLP may suffer from the large amount of computation in the training phase. In this paper, we establish a comparison between MLP and CNN. We provide MLP with the UCD descriptor and the appropriate network configuration. For CNN, we employ the convolutional network designed for handwritten and machine-printed character recognition (Lenet-5) and we adapt it to support 62 classes, including both digits and characters. In addition, GPU parallelization is studied to speed up both of MLP and CNN classifiers. Based on our experimentations, we demonstrate that the used real-time CNN is 2x more relevant than MLP when classifying characters.

Handwritten-word spotting using biologically inspired features.

PubMed

van der Zant, Tijn; Schomaker, Lambert; Haak, Koen

2008-11-01

For quick access to new handwritten collections, current handwriting recognition methods are too cumbersome. They cannot deal with the lack of labeled data and would require extensive laboratory training for each individual script, style, language and collection. We propose a biologically inspired whole-word recognition method which is used to incrementally elicit word labels in a live, web-based annotation system, named Monk. Since human labor should be minimized given the massive amount of image data, it becomes important to rely on robust perceptual mechanisms in the machine. Recent computational models of the neuro-physiology of vision are applied to isolated word classification. A primate cortex-like mechanism allows to classify text-images that have a low frequency of occurrence. Typically these images are the most difficult to retrieve and often contain named entities and are regarded as the most important to people. Usually standard pattern-recognition technology cannot deal with these text-images if there are not enough labeled instances. The results of this retrieval system are compared to normalized word-image matching and appear to be very promising.

Memristor-Based Analog Computation and Neural Network Classification with a Dot Product Engine.

PubMed

Hu, Miao; Graves, Catherine E; Li, Can; Li, Yunning; Ge, Ning; Montgomery, Eric; Davila, Noraica; Jiang, Hao; Williams, R Stanley; Yang, J Joshua; Xia, Qiangfei; Strachan, John Paul

2018-03-01

Using memristor crossbar arrays to accelerate computations is a promising approach to efficiently implement algorithms in deep neural networks. Early demonstrations, however, are limited to simulations or small-scale problems primarily due to materials and device challenges that limit the size of the memristor crossbar arrays that can be reliably programmed to stable and analog values, which is the focus of the current work. High-precision analog tuning and control of memristor cells across a 128 × 64 array is demonstrated, and the resulting vector matrix multiplication (VMM) computing precision is evaluated. Single-layer neural network inference is performed in these arrays, and the performance compared to a digital approach is assessed. Memristor computing system used here reaches a VMM accuracy equivalent of 6 bits, and an 89.9% recognition accuracy is achieved for the 10k MNIST handwritten digit test set. Forecasts show that with integrated (on chip) and scaled memristors, a computational efficiency greater than 100 trillion operations per second per Watt is possible. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Font generation of personal handwritten Chinese characters

NASA Astrophysics Data System (ADS)

Lin, Jeng-Wei; Wang, Chih-Yin; Ting, Chao-Lung; Chang, Ray-I.

2014-01-01

Today, digital multimedia messages have drawn more and more attention due to the great achievement of computer and network techniques. Nevertheless, text is still the most popular media for people to communicate with others. Many fonts have been developed so that product designers can choose unique fonts to demonstrate their idea gracefully. It is commonly believed that handwritings can reflect one's personality, emotion, feeling, education level, and so on. This is especially true in Chinese calligraphy. However, it is not easy for ordinary users to customize a font of their personal handwritings. In this study, we performed a process reengineering in font generation. We present a new method to create font in a batch mode. Rather than to create glyphs of characters one by one according to their codepoints, people create glyphs incrementally in an on-demand manner. A Java Implementation is developed to read a document image of user handwritten Chinese characters, and make a vector font of these handwritten Chinese characters. Preliminary experiment result shows that the proposed method can help ordinary users create their personal handwritten fonts easily and quickly.

Recognition of handwritten similar Chinese characters by self-growing probabilistic decision-based neural network.

PubMed

Fu, H C; Xu, Y Y; Chang, H Y

1999-12-01

Recognition of similar (confusion) characters is a difficult problem in optical character recognition (OCR). In this paper, we introduce a neural network solution that is capable of modeling minor differences among similar characters, and is robust to various personal handwriting styles. The Self-growing Probabilistic Decision-based Neural Network (SPDNN) is a probabilistic type neural network, which adopts a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment scheme. Based on the SPDNN model, we have constructed a three-stage recognition system. First, a coarse classifier determines a character to be input to one of the pre-defined subclasses partitioned from a large character set, such as Chinese mixed with alphanumerics. Then a character recognizer determines the input image which best matches the reference character in the subclass. Lastly, the third module is a similar character recognizer, which can further enhance the recognition accuracy among similar or confusing characters. The prototype system has demonstrated a successful application of SPDNN to similar handwritten Chinese recognition for the public database CCL/HCCR1 (5401 characters x200 samples). Regarding performance, experiments on the CCL/HCCR1 database produced 90.12% recognition accuracy with no rejection, and 94.11% accuracy with 6.7% rejection, respectively. This recognition accuracy represents about 4% improvement on the previously announced performance. As to processing speed, processing before recognition (including image preprocessing, segmentation, and feature extraction) requires about one second for an A4 size character image, and recognition consumes approximately 0.27 second per character on a Pentium-100 based personal computer, without use of any hardware accelerator or co-processor.

U.S. Army Research Laboratory (ARL) Corporate Dari Document Transcription and Translation Guidelines

DTIC Science & Technology

2012-10-01

text file format. 15. SUBJECT TERMS Transcription, Translation, guidelines, ground truth, Optical character recognition , OCR, Machine Translation, MT...foreign language into a target language in order to train, test, and evaluate optical character recognition (OCR) and machine translation (MT) embedded...graphic element and should not be transcribed. Elements that are not part of the primary text such as handwritten annotations or stamps should not be

Learning and Inductive Inference

DTIC Science & Technology

1982-07-01

a set of graph grammars to describe visual scenes . Other researchers have applied graph grammars to the pattern recognition of handwritten characters...345 1. Issues / 345 2. Mostows’ operationalizer / 350 0. Learning from ezamples / 360 1. Issues / 3t60 2. Learning in control and pattern recognition ...art.icleis on rote learntinig and ailvice- tAik g. K(ennieth Clarkson contributed Ltte article on grmvit atical inference, anid Geoff’ lroiney wrote

Learning optimal features for visual pattern recognition

NASA Astrophysics Data System (ADS)

Labusch, Kai; Siewert, Udo; Martinetz, Thomas; Barth, Erhardt

2007-02-01

The optimal coding hypothesis proposes that the human visual system has adapted to the statistical properties of the environment by the use of relatively simple optimality criteria. We here (i) discuss how the properties of different models of image coding, i.e. sparseness, decorrelation, and statistical independence are related to each other (ii) propose to evaluate the different models by verifiable performance measures (iii) analyse the classification performance on images of handwritten digits (MNIST data base). We first employ the SPARSENET algorithm (Olshausen, 1998) to derive a local filter basis (on 13 × 13 pixels windows). We then filter the images in the database (28 × 28 pixels images of digits) and reduce the dimensionality of the resulting feature space by selecting the locally maximal filter responses. We then train a support vector machine on a training set to classify the digits and report results obtained on a separate test set. Currently, the best state-of-the-art result on the MNIST data base has an error rate of 0,4%. This result, however, has been obtained by using explicit knowledge that is specific to the data (elastic distortion model for digits). We here obtain an error rate of 0,55% which is second best but does not use explicit data specific knowledge. In particular it outperforms by far all methods that do not use data-specific knowledge.

Arabic handwritten: pre-processing and segmentation

NASA Astrophysics Data System (ADS)

Maliki, Makki; Jassim, Sabah; Al-Jawad, Naseer; Sellahewa, Harin

2012-06-01

This paper is concerned with pre-processing and segmentation tasks that influence the performance of Optical Character Recognition (OCR) systems and handwritten/printed text recognition. In Arabic, these tasks are adversely effected by the fact that many words are made up of sub-words, with many sub-words there associated one or more diacritics that are not connected to the sub-word's body; there could be multiple instances of sub-words overlap. To overcome these problems we investigate and develop segmentation techniques that first segment a document into sub-words, link the diacritics with their sub-words, and removes possible overlapping between words and sub-words. We shall also investigate two approaches for pre-processing tasks to estimate sub-words baseline, and to determine parameters that yield appropriate slope correction, slant removal. We shall investigate the use of linear regression on sub-words pixels to determine their central x and y coordinates, as well as their high density part. We also develop a new incremental rotation procedure to be performed on sub-words that determines the best rotation angle needed to realign baselines. We shall demonstrate the benefits of these proposals by conducting extensive experiments on publicly available databases and in-house created databases. These algorithms help improve character segmentation accuracy by transforming handwritten Arabic text into a form that could benefit from analysis of printed text.

ASM Based Synthesis of Handwritten Arabic Text Pages

PubMed Central

Al-Hamadi, Ayoub; Elzobi, Moftah; El-etriby, Sherif; Ghoneim, Ahmed

2015-01-01

Document analysis tasks, as text recognition, word spotting, or segmentation, are highly dependent on comprehensive and suitable databases for training and validation. However their generation is expensive in sense of labor and time. As a matter of fact, there is a lack of such databases, which complicates research and development. This is especially true for the case of Arabic handwriting recognition, that involves different preprocessing, segmentation, and recognition methods, which have individual demands on samples and ground truth. To bypass this problem, we present an efficient system that automatically turns Arabic Unicode text into synthetic images of handwritten documents and detailed ground truth. Active Shape Models (ASMs) based on 28046 online samples were used for character synthesis and statistical properties were extracted from the IESK-arDB database to simulate baselines and word slant or skew. In the synthesis step ASM based representations are composed to words and text pages, smoothed by B-Spline interpolation and rendered considering writing speed and pen characteristics. Finally, we use the synthetic data to validate a segmentation method. An experimental comparison with the IESK-arDB database encourages to train and test document analysis related methods on synthetic samples, whenever no sufficient natural ground truthed data is available. PMID:26295059

ASM Based Synthesis of Handwritten Arabic Text Pages.

PubMed

Dinges, Laslo; Al-Hamadi, Ayoub; Elzobi, Moftah; El-Etriby, Sherif; Ghoneim, Ahmed

2015-01-01

Document analysis tasks, as text recognition, word spotting, or segmentation, are highly dependent on comprehensive and suitable databases for training and validation. However their generation is expensive in sense of labor and time. As a matter of fact, there is a lack of such databases, which complicates research and development. This is especially true for the case of Arabic handwriting recognition, that involves different preprocessing, segmentation, and recognition methods, which have individual demands on samples and ground truth. To bypass this problem, we present an efficient system that automatically turns Arabic Unicode text into synthetic images of handwritten documents and detailed ground truth. Active Shape Models (ASMs) based on 28046 online samples were used for character synthesis and statistical properties were extracted from the IESK-arDB database to simulate baselines and word slant or skew. In the synthesis step ASM based representations are composed to words and text pages, smoothed by B-Spline interpolation and rendered considering writing speed and pen characteristics. Finally, we use the synthetic data to validate a segmentation method. An experimental comparison with the IESK-arDB database encourages to train and test document analysis related methods on synthetic samples, whenever no sufficient natural ground truthed data is available.

Boosting bonsai trees for handwritten/printed text discrimination

NASA Astrophysics Data System (ADS)

Ricquebourg, Yann; Raymond, Christian; Poirriez, Baptiste; Lemaitre, Aurélie; Coüasnon, Bertrand

2013-12-01

Boosting over decision-stumps proved its efficiency in Natural Language Processing essentially with symbolic features, and its good properties (fast, few and not critical parameters, not sensitive to over-fitting) could be of great interest in the numeric world of pixel images. In this article we investigated the use of boosting over small decision trees, in image classification processing, for the discrimination of handwritten/printed text. Then, we conducted experiments to compare it to usual SVM-based classification revealing convincing results with very close performance, but with faster predictions and behaving far less as a black-box. Those promising results tend to make use of this classifier in more complex recognition tasks like multiclass problems.

Textual blocks rectification method based on fast Hough transform analysis in identity documents recognition

NASA Astrophysics Data System (ADS)

Bezmaternykh, P. V.; Nikolaev, D. P.; Arlazarov, V. L.

2018-04-01

Textual blocks rectification or slant correction is an important stage of document image processing in OCR systems. This paper considers existing methods and introduces an approach for the construction of such algorithms based on Fast Hough Transform analysis. A quality measurement technique is proposed and obtained results are shown for both printed and handwritten textual blocks processing as a part of an industrial system of identity documents recognition on mobile devices.

Dimension Reduction With Extreme Learning Machine.

PubMed

Kasun, Liyanaarachchi Lekamalage Chamara; Yang, Yan; Huang, Guang-Bin; Zhang, Zhengyou

2016-08-01

Data may often contain noise or irrelevant information, which negatively affect the generalization capability of machine learning algorithms. The objective of dimension reduction algorithms, such as principal component analysis (PCA), non-negative matrix factorization (NMF), random projection (RP), and auto-encoder (AE), is to reduce the noise or irrelevant information of the data. The features of PCA (eigenvectors) and linear AE are not able to represent data as parts (e.g. nose in a face image). On the other hand, NMF and non-linear AE are maimed by slow learning speed and RP only represents a subspace of original data. This paper introduces a dimension reduction framework which to some extend represents data as parts, has fast learning speed, and learns the between-class scatter subspace. To this end, this paper investigates a linear and non-linear dimension reduction framework referred to as extreme learning machine AE (ELM-AE) and sparse ELM-AE (SELM-AE). In contrast to tied weight AE, the hidden neurons in ELM-AE and SELM-AE need not be tuned, and their parameters (e.g, input weights in additive neurons) are initialized using orthogonal and sparse random weights, respectively. Experimental results on USPS handwritten digit recognition data set, CIFAR-10 object recognition, and NORB object recognition data set show the efficacy of linear and non-linear ELM-AE and SELM-AE in terms of discriminative capability, sparsity, training time, and normalized mean square error.

Giro form reading machine

NASA Astrophysics Data System (ADS)

Minh Ha, Thien; Niggeler, Dieter; Bunke, Horst; Clarinval, Jose

1995-08-01

Although giro forms are used by many people in daily life for money remittance in Switzerland, the processing of these forms at banks and post offices is only partly automated. We describe an ongoing project for building an automatic system that is able to recognize various items printed or written on a giro form. The system comprises three main components, namely, an automatic form feeder, a camera system, and a computer. These components are connected in such a way that the system is able to process a bunch of forms without any human interactions. We present two real applications of our system in the field of payment services, which require the reading of both machine printed and handwritten information that may appear on a giro form. One particular feature of giro forms is their flexible layout, i.e., information items are located differently from one form to another, thus requiring an additional analysis step to localize them before recognition. A commercial optical character recognition software package is used for recognition of machine-printed information, whereas handwritten information is read by our own algorithms, the details of which are presented. The system is implemented by using a client/server architecture providing a high degree of flexibility to change. Preliminary results are reported supporting our claim that the system is usable in practice.

A Novel Handwritten Letter Recognizer Using Enhanced Evolutionary Neural Network

NASA Astrophysics Data System (ADS)

Mahmoudi, Fariborz; Mirzashaeri, Mohsen; Shahamatnia, Ehsan; Faridnia, Saed

This paper introduces a novel design for handwritten letter recognition by employing a hybrid back-propagation neural network with an enhanced evolutionary algorithm. Feeding the neural network consists of a new approach which is invariant to translation, rotation, and scaling of input letters. Evolutionary algorithm is used for the global search of the search space and the back-propagation algorithm is used for the local search. The results have been computed by implementing this approach for recognizing 26 English capital letters in the handwritings of different people. The computational results show that the neural network reaches very satisfying results with relatively scarce input data and a promising performance improvement in convergence of the hybrid evolutionary back-propagation algorithms is exhibited.

Aspects of quality insurance in digitizing historical climate data in Germany

NASA Astrophysics Data System (ADS)

Mächel, H.; Behrends, J.; Kapala, A.

2010-09-01

This contribution presents some of the problems and offers solutions regarding the digitization of historical meteorological data, and explains the need for verification and quality control. For the assessment of changes in climate extremes, long-term and complete observational records with a high temporal resolution are needed. However, in most countries, including Germany, such climate data are rare. Therefore, in 2005, the German Weather Service launched a project to inventory and digitize historical daily climatic records in cooperation with the Meteorological Institute of the University of Bonn. Experience with Optical Character Recognition (OCR) show that it is only of very limited use, as even printed tables (e.g. yearbooks) are not sufficiently recognized (10-20% error). In hand-written records, the recognition rate is about 50%. By comparing daily and monthly values, it is possible to auto-detect errors, but they can not be automatically corrected, since there is often more than one error per month. These erroneous data must then be controlled manually on an individual basis, which is significantly more error-prone than direct manual input. Therefore, both precipitation and climate station data are digitized manually. The time required to digitize one year of precipitation data (including the recording of daily precipitation amount and type, snow amount and type, and weather events such as thunder storms, fog, etc.) is equivalent to about five hours for one year of data. This involves manually typing, reformatting and quality control of the digitized data, as well as creating a digital photograph. For climate stations with three observations per day, the working time is 30-50 hours for one year of data, depending on the number of parameters and the condition of the documents. Several other problems occur when creating the digital records from historical observational data, some of which are listed below. Older records often used varying units and different conventions. For example, a value of 100 was added to the observed temperatures to avoid negative values. Furthermore, because standardization of the observations was very low when measurements began up to 200 years ago, the data often reflect a greater part of non-climatic influences. Varying daily observation times make it difficult to calculate a representative daily value. Even unconventional completed tables cost labor and requires experienced and trained staff. Data homogenization as well as both manual and automatic quality control may address some of these problems.

A smart sensor architecture based on emergent computation in an array of outer-totalistic cells

NASA Astrophysics Data System (ADS)

Dogaru, Radu; Dogaru, Ioana; Glesner, Manfred

2005-06-01

A novel smart-sensor architecture is proposed, capable to segment and recognize characters in a monochrome image. It is capable to provide a list of ASCII codes representing the recognized characters from the monochrome visual field. It can operate as a blind's aid or for industrial applications. A bio-inspired cellular model with simple linear neurons was found the best to perform the nontrivial task of cropping isolated compact objects such as handwritten digits or characters. By attaching a simple outer-totalistic cell to each pixel sensor, emergent computation in the resulting cellular automata lattice provides a straightforward and compact solution to the otherwise computationally intensive problem of character segmentation. A simple and robust recognition algorithm is built in a compact sequential controller accessing the array of cells so that the integrated device can provide directly a list of codes of the recognized characters. Preliminary simulation tests indicate good performance and robustness to various distortions of the visual field.

Information based universal feature extraction

NASA Astrophysics Data System (ADS)

Amiri, Mohammad; Brause, Rüdiger

2015-02-01

In many real world image based pattern recognition tasks, the extraction and usage of task-relevant features are the most crucial part of the diagnosis. In the standard approach, they mostly remain task-specific, although humans who perform such a task always use the same image features, trained in early childhood. It seems that universal feature sets exist, but they are not yet systematically found. In our contribution, we tried to find those universal image feature sets that are valuable for most image related tasks. In our approach, we trained a neural network by natural and non-natural images of objects and background, using a Shannon information-based algorithm and learning constraints. The goal was to extract those features that give the most valuable information for classification of visual objects hand-written digits. This will give a good start and performance increase for all other image learning tasks, implementing a transfer learning approach. As result, in our case we found that we could indeed extract features which are valid in all three kinds of tasks.

Geometrical structure of Neural Networks: Geodesics, Jeffrey's Prior and Hyper-ribbons

NASA Astrophysics Data System (ADS)

Hayden, Lorien; Alemi, Alex; Sethna, James

2014-03-01

Neural networks are learning algorithms which are employed in a host of Machine Learning problems including speech recognition, object classification and data mining. In practice, neural networks learn a low dimensional representation of high dimensional data and define a model manifold which is an embedding of this low dimensional structure in the higher dimensional space. In this work, we explore the geometrical structure of a neural network model manifold. A Stacked Denoising Autoencoder and a Deep Belief Network are trained on handwritten digits from the MNIST database. Construction of geodesics along the surface and of slices taken from the high dimensional manifolds reveal a hierarchy of widths corresponding to a hyper-ribbon structure. This property indicates that neural networks fall into the class of sloppy models, in which certain parameter combinations dominate the behavior. Employing this information could prove valuable in designing both neural network architectures and training algorithms. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No . DGE-1144153.

PCANet: A Simple Deep Learning Baseline for Image Classification?

PubMed

Chan, Tsung-Han; Jia, Kui; Gao, Shenghua; Lu, Jiwen; Zeng, Zinan; Ma, Yi

2015-12-01

In this paper, we propose a very simple deep learning network for image classification that is based on very basic data processing components: 1) cascaded principal component analysis (PCA); 2) binary hashing; and 3) blockwise histograms. In the proposed architecture, the PCA is employed to learn multistage filter banks. This is followed by simple binary hashing and block histograms for indexing and pooling. This architecture is thus called the PCA network (PCANet) and can be extremely easily and efficiently designed and learned. For comparison and to provide a better understanding, we also introduce and study two simple variations of PCANet: 1) RandNet and 2) LDANet. They share the same topology as PCANet, but their cascaded filters are either randomly selected or learned from linear discriminant analysis. We have extensively tested these basic networks on many benchmark visual data sets for different tasks, including Labeled Faces in the Wild (LFW) for face verification; the MultiPIE, Extended Yale B, AR, Facial Recognition Technology (FERET) data sets for face recognition; and MNIST for hand-written digit recognition. Surprisingly, for all tasks, such a seemingly naive PCANet model is on par with the state-of-the-art features either prefixed, highly hand-crafted, or carefully learned [by deep neural networks (DNNs)]. Even more surprisingly, the model sets new records for many classification tasks on the Extended Yale B, AR, and FERET data sets and on MNIST variations. Additional experiments on other public data sets also demonstrate the potential of PCANet to serve as a simple but highly competitive baseline for texture classification and object recognition.

Arabic Optical Character Recognition (OCR) Evaluation in Order to Develop a Post-OCR Module

DTIC Science & Technology

2011-09-01

handwritten, and many more have some handwriting in the margins. Some images are blurred or faded to the point of illegibility. Others are mostly or...it is to English, because Arabic has more features such as agreement. We say that Arabic is more “morphologically rich” than English. We intend to

Randomized Prediction Games for Adversarial Machine Learning.

PubMed

Rota Bulo, Samuel; Biggio, Battista; Pillai, Ignazio; Pelillo, Marcello; Roli, Fabio

In spam and malware detection, attackers exploit randomization to obfuscate malicious data and increase their chances of evading detection at test time, e.g., malware code is typically obfuscated using random strings or byte sequences to hide known exploits. Interestingly, randomization has also been proposed to improve security of learning algorithms against evasion attacks, as it results in hiding information about the classifier to the attacker. Recent work has proposed game-theoretical formulations to learn secure classifiers, by simulating different evasion attacks and modifying the classification function accordingly. However, both the classification function and the simulated data manipulations have been modeled in a deterministic manner, without accounting for any form of randomization. In this paper, we overcome this limitation by proposing a randomized prediction game, namely, a noncooperative game-theoretic formulation in which the classifier and the attacker make randomized strategy selections according to some probability distribution defined over the respective strategy set. We show that our approach allows one to improve the tradeoff between attack detection and false alarms with respect to the state-of-the-art secure classifiers, even against attacks that are different from those hypothesized during design, on application examples including handwritten digit recognition, spam, and malware detection.In spam and malware detection, attackers exploit randomization to obfuscate malicious data and increase their chances of evading detection at test time, e.g., malware code is typically obfuscated using random strings or byte sequences to hide known exploits. Interestingly, randomization has also been proposed to improve security of learning algorithms against evasion attacks, as it results in hiding information about the classifier to the attacker. Recent work has proposed game-theoretical formulations to learn secure classifiers, by simulating different evasion attacks and modifying the classification function accordingly. However, both the classification function and the simulated data manipulations have been modeled in a deterministic manner, without accounting for any form of randomization. In this paper, we overcome this limitation by proposing a randomized prediction game, namely, a noncooperative game-theoretic formulation in which the classifier and the attacker make randomized strategy selections according to some probability distribution defined over the respective strategy set. We show that our approach allows one to improve the tradeoff between attack detection and false alarms with respect to the state-of-the-art secure classifiers, even against attacks that are different from those hypothesized during design, on application examples including handwritten digit recognition, spam, and malware detection.

Preliminary study towards the development of copying skill assessment on dyslexic children in Jawi handwriting

NASA Astrophysics Data System (ADS)

Rahim, Kartini Abdul; Kahar, Rosmila Abdul; Khalid, Halimi Mohd.; Salleh, Rohayu Mohd; Hashim, Rathiah

2015-05-01

Recognition of Arabic handwritten and its variants such as Farsi (Persian) and Urdu had been receiving considerable attention in recent years. Being contrast to Arabic handwritten, Jawi, as a second method of Malay handwritten, has not been studied yet, but if any, there were a few references on it. The recent transformation in Malaysian education, the Special Education is one of the priorities in the Malaysia Blueprint. One of the special needs quoted in Malaysia education is dyslexia. A dyslexic student is considered as student with learning disability. Concluding a student is truly dyslexia might be incorrect for they were only assessed through Roman alphabet, without considering assessment via Jawi handwriting. A study was conducted on dyslexic students attending a special class for dyslexia in Malay Language to determine whether they are also dyslexia in Jawi handwriting. The focus of the study is to test the copying skills in relation to word reading and writing in Malay Language with and without dyslexia through both characters. A total of 10 dyslexic children and 10 normal children were recruited. In conclusion for future study, dyslexic students have less difficulty in performing Jawi handwriting in Malay Language through statistical analysis.

Digital Management of a Hysteroscopy Surgery Using Parts of the SNOMED Medical Model

PubMed Central

Kollias, Anastasios; Paschopoulos, Minas; Evangelou, Angelos; Poulos, Marios

2012-01-01

This work describes a hysteroscopy surgery management application that was designed based on the medical information standard SNOMED. We describe how the application fulfils the needs of this procedure and the way in which existing handwritten medical information is effectively transmitted to the application’s database. PMID:22848338

Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification

PubMed Central

Yang, Xinyi

2016-01-01

In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods. PMID:27610128

Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification.

PubMed

Pang, Shan; Yang, Xinyi

2016-01-01

In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods.

Retrieving handwriting by combining word spotting and manifold ranking

NASA Astrophysics Data System (ADS)

Peña Saldarriaga, Sebastián; Morin, Emmanuel; Viard-Gaudin, Christian

2012-01-01

Online handwritten data, produced with Tablet PCs or digital pens, consists in a sequence of points (x, y). As the amount of data available in this form increases, algorithms for retrieval of online data are needed. Word spotting is a common approach used for the retrieval of handwriting. However, from an information retrieval (IR) perspective, word spotting is a primitive keyword based matching and retrieval strategy. We propose a framework for handwriting retrieval where an arbitrary word spotting method is used, and then a manifold ranking algorithm is applied on the initial retrieval scores. Experimental results on a database of more than 2,000 handwritten newswires show that our method can improve the performances of a state-of-the-art word spotting system by more than 10%.

Signature Verification Based on Handwritten Text Recognition

NASA Astrophysics Data System (ADS)

Viriri, Serestina; Tapamo, Jules-R.

Signatures continue to be an important biometric trait because it remains widely used primarily for authenticating the identity of human beings. This paper presents an efficient text-based directional signature recognition algorithm which verifies signatures, even when they are composed of special unconstrained cursive characters which are superimposed and embellished. This algorithm extends the character-based signature verification technique. The experiments carried out on the GPDS signature database and an additional database created from signatures captured using the ePadInk tablet, show that the approach is effective and efficient, with a positive verification rate of 94.95%.

Sea Level Data Archaeology for the Global Sea Level Observing System (GLOSS)

NASA Astrophysics Data System (ADS)

Bradshaw, Elizabeth; Matthews, Andy; Rickards, Lesley; Jevrejeva, Svetlana

2015-04-01

The Global Sea Level Observing System (GLOSS) was set up in 1985 to collect long term tide gauge observations and has carried out a number of data archaeology activities over the past decade, including sending member organisations questionnaires to report on their repositories. The GLOSS Group of Experts (GLOSS GE) is looking to future developments in sea level data archaeology and will provide its user community with guidance on finding, digitising, quality controlling and distributing historic records. Many records may not be held in organisational archives and may instead by in national libraries, archives and other collections. GLOSS will promote a Citizen Science approach to discovering long term records by providing tools for volunteers to report data. Tide gauge data come in two different formats, charts and hand-written ledgers. Charts are paper analogue records generated by the mechanical instrument driving a pen trace. Several GLOSS members have developed software to automatically digitise these charts and the various methods were reported in a paper on automated techniques for the digitization of archived mareograms, delivered to the GLOSS GE 13th meeting. GLOSS is creating a repository of software for scanning analogue charts. NUNIEAU is the only publically available software for digitising tide gauge charts but other organisations have developed their own tide gauge digitising software that is available internally. There are several other freely available software packages that convert image data to numerical values. GLOSS could coordinate a comparison study of the various different digitising software programs by: Sending the same charts to each organisation and asking everyone to digitise them using their own procedures Comparing the digitised data Providing recommendations to the GLOSS community The other major form of analogue sea level data is handwritten ledgers, which are usually observations of high and low waters, but sometimes contain higher frequency data. The standard current method for digitising these data is to enter the values manually, which has been performed by GLOSS countries, including France and Spain. The GLOSS GE is exploring other methods for use in the future as this process is time consuming. Current projects to improve Handwritten Text Recognition (HTR) tend to be working with the written word and so require knowledge of sentence structures and word occurrence probabilities to reconstruct sentences e.g. tranScriptorium (European Union's Seventh Framework Programme funded project). This approach would not be applicable to sea level data, however tidal data by its very nature contains periodicity and predictability. HTR technology could be adapted to take this into account and improve the automatic digitisation of handwritten tide gauge ledgers. There are many challenges facing the sea level data archaeology community, but it is hoped that improvements in technology can overcome some of the obstacles: Faster automated digitisation of tide gauge charts Minimal user input Automatic transcribing of handwritten ledgers The GLOSS GE will provide a central location to share software, guidelines for quality controlling data and the GLOSS data archive centres will be the repository of the newly created datasets.

Progressive sparse representation-based classification using local discrete cosine transform evaluation for image recognition

NASA Astrophysics Data System (ADS)

Song, Xiaoning; Feng, Zhen-Hua; Hu, Guosheng; Yang, Xibei; Yang, Jingyu; Qi, Yunsong

2015-09-01

This paper proposes a progressive sparse representation-based classification algorithm using local discrete cosine transform (DCT) evaluation to perform face recognition. Specifically, the sum of the contributions of all training samples of each subject is first taken as the contribution of this subject, then the redundant subject with the smallest contribution to the test sample is iteratively eliminated. Second, the progressive method aims at representing the test sample as a linear combination of all the remaining training samples, by which the representation capability of each training sample is exploited to determine the optimal "nearest neighbors" for the test sample. Third, the transformed DCT evaluation is constructed to measure the similarity between the test sample and each local training sample using cosine distance metrics in the DCT domain. The final goal of the proposed method is to determine an optimal weighted sum of nearest neighbors that are obtained under the local correlative degree evaluation, which is approximately equal to the test sample, and we can use this weighted linear combination to perform robust classification. Experimental results conducted on the ORL database of faces (created by the Olivetti Research Laboratory in Cambridge), the FERET face database (managed by the Defense Advanced Research Projects Agency and the National Institute of Standards and Technology), AR face database (created by Aleix Martinez and Robert Benavente in the Computer Vision Center at U.A.B), and USPS handwritten digit database (gathered at the Center of Excellence in Document Analysis and Recognition at SUNY Buffalo) demonstrate the effectiveness of the proposed method.

Multi-exemplar affinity propagation.

PubMed

Wang, Chang-Dong; Lai, Jian-Huang; Suen, Ching Y; Zhu, Jun-Yong

2013-09-01

The affinity propagation (AP) clustering algorithm has received much attention in the past few years. AP is appealing because it is efficient, insensitive to initialization, and it produces clusters at a lower error rate than other exemplar-based methods. However, its single-exemplar model becomes inadequate when applied to model multisubclasses in some situations such as scene analysis and character recognition. To remedy this deficiency, we have extended the single-exemplar model to a multi-exemplar one to create a new multi-exemplar affinity propagation (MEAP) algorithm. This new model automatically determines the number of exemplars in each cluster associated with a super exemplar to approximate the subclasses in the category. Solving the model is NP-hard and we tackle it with the max-sum belief propagation to produce neighborhood maximum clusters, with no need to specify beforehand the number of clusters, multi-exemplars, and superexemplars. Also, utilizing the sparsity in the data, we are able to reduce substantially the computational time and storage. Experimental studies have shown MEAP's significant improvements over other algorithms on unsupervised image categorization and the clustering of handwritten digits.

CW-SSIM kernel based random forest for image classification

NASA Astrophysics Data System (ADS)

Fan, Guangzhe; Wang, Zhou; Wang, Jiheng

2010-07-01

Complex wavelet structural similarity (CW-SSIM) index has been proposed as a powerful image similarity metric that is robust to translation, scaling and rotation of images, but how to employ it in image classification applications has not been deeply investigated. In this paper, we incorporate CW-SSIM as a kernel function into a random forest learning algorithm. This leads to a novel image classification approach that does not require a feature extraction or dimension reduction stage at the front end. We use hand-written digit recognition as an example to demonstrate our algorithm. We compare the performance of the proposed approach with random forest learning based on other kernels, including the widely adopted Gaussian and the inner product kernels. Empirical evidences show that the proposed method is superior in its classification power. We also compared our proposed approach with the direct random forest method without kernel and the popular kernel-learning method support vector machine. Our test results based on both simulated and realworld data suggest that the proposed approach works superior to traditional methods without the feature selection procedure.

Quantify spatial relations to discover handwritten graphical symbols

NASA Astrophysics Data System (ADS)

Li, Jinpeng; Mouchère, Harold; Viard-Gaudin, Christian

2012-01-01

To model a handwritten graphical language, spatial relations describe how the strokes are positioned in the 2-dimensional space. Most of existing handwriting recognition systems make use of some predefined spatial relations. However, considering a complex graphical language, it is hard to express manually all the spatial relations. Another possibility would be to use a clustering technique to discover the spatial relations. In this paper, we discuss how to create a relational graph between strokes (nodes) labeled with graphemes in a graphical language. Then we vectorize spatial relations (edges) for clustering and quantization. As the targeted application, we extract the repetitive sub-graphs (graphical symbols) composed of graphemes and learned spatial relations. On two handwriting databases, a simple mathematical expression database and a complex flowchart database, the unsupervised spatial relations outperform the predefined spatial relations. In addition, we visualize the frequent patterns on two text-lines containing Chinese characters.

Development of a Digitalized Child's Checkups Information System.

PubMed

Ito, Yoshiya; Takimoto, Hidemi

2017-01-01

In Japan, health checkups for children take place from infancy through high school and play an important role in the maintenance and control of childhood/adolescent health. The anthropometric data obtained during these checkups are kept in health centers and schools and are also recorded in a mother's maternal and child health handbook, as well as on school health cards. These data are meaningful if they are utilized well and in an appropriate manner. They are particularly useful for the prevention of obesity-related conditions in adulthood, such as metabolic syndrome and diabetes mellitus. For this purpose, we have tried to establish a scanning system with an optical character recognition (OCR) function, which links data obtained during health checkups in infancy with that obtained in schools. In this system, handwritten characters on the records are scanned and processed using OCR. However, because many of the scanned characters are not read properly, we must wait for the improvement in the performance of the OCR function. In addition, we have developed Microsoft Excel spreadsheets, on which obesity-related indices, such as body mass index and relative body weight, are calculated. These sheets also provide functions that tabulate the frequencies of obesity in specific groups. Actively using these data and digitalized systems will not only contribute towards resolving physical health problems in children, but also decrease the risk of developing lifestyle-related diseases in adulthood.

Fusion of Dependent and Independent Biometric Information Sources

DTIC Science & Technology

2005-03-01

palmprint , DNA, ECG, signature, etc. The comparison of various biometric techniques is given in [13] and is presented in Table 1. Since, each...theory. Experimental studies on the M2VTS database [32] showed that a reduction in error rates is up to about 40%. Four combination strategies are...taken from the CEDAR benchmark database . The word recognition results were the highest (91%) among published results for handwritten words (before 2001

Artificial neural networks for document analysis and recognition.

PubMed

Marinai, Simone; Gori, Marco; Soda, Giovanni; Society, Computer

2005-01-01

Artificial neural networks have been extensively applied to document analysis and recognition. Most efforts have been devoted to the recognition of isolated handwritten and printed characters with widely recognized successful results. However, many other document processing tasks, like preprocessing, layout analysis, character segmentation, word recognition, and signature verification, have been effectively faced with very promising results. This paper surveys the most significant problems in the area of offline document image processing, where connectionist-based approaches have been applied. Similarities and differences between approaches belonging to different categories are discussed. A particular emphasis is given on the crucial role of prior knowledge for the conception of both appropriate architectures and learning algorithms. Finally, the paper provides a critical analysis on the reviewed approaches and depicts the most promising research guidelines in the field. In particular, a second generation of connectionist-based models are foreseen which are based on appropriate graphical representations of the learning environment.

Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets

DTIC Science & Technology

2015-04-24

Feature Representations usingProbabilistic Quadtrees and Deep Belief Nets Learning sparse feature representations is a useful instru- ment for solving an...novel framework for the classifi cation of handwritten digits that learns sparse representations using probabilistic quadtrees and Deep Belief Nets... Learning Sparse Feature Representations usingProbabilistic Quadtrees and Deep Belief Nets Report Title Learning sparse feature representations is a useful

Word Spotting and Recognition with Embedded Attributes.

PubMed

Almazán, Jon; Gordo, Albert; Fornés, Alicia; Valveny, Ernest

2014-12-01

This paper addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks.

Orthographic and phonological neighborhood effects in handwritten word perception

PubMed Central

Goldinger, Stephen D.

2017-01-01

In printed-word perception, the orthographic neighborhood effect (i.e., faster recognition of words with more neighbors) has considerable theoretical importance, because it implicates great interactivity in lexical access. Mulatti, Reynolds, and Besner Journal of Experimental Psychology: Human Perception and Performance, 32, 799–810 (2006) questioned the validity of orthographic neighborhood effects, suggesting that they reflect a confound with phonological neighborhood density. They reported that, when phonological density is controlled, orthographic neighborhood effects vanish. Conversely, phonological neighborhood effects were still evident even when controlling for orthographic neighborhood density. The present study was a replication and extension of Mulatti et al. (2006), with words presented in four different formats (computer-generated print and cursive, and handwritten print and cursive). The results from Mulatti et al. (2006) were replicated with computer-generated stimuli, but were reversed with natural stimuli. These results suggest that, when ambiguity is introduced at the level of individual letters, top-down influences from lexical neighbors are increased. PMID:26306881

A paper form processing system with an error correcting function for reading handwritten Kanji strings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Katsumi Marukawa; Kazuki Nakashima; Masashi Koga

1994-12-31

This paper presents a paper form processing system with an error correcting function for reading handwritten kanji strings. In the paper form processing system, names and addresses are important key data, and especially this paper takes up an error correcting method for name and address recognition. The method automatically corrects errors of the kanji OCR (Optical Character Reader) with the help of word dictionaries and other knowledge. Moreover, it allows names and addresses to be written in any style. The method consists of word matching {open_quotes}furigana{close_quotes} verification for name strings, and address approval for address strings. For word matching, kanjimore » name candidates are extracted by automaton-type word matching. In {open_quotes}furigana{close_quotes} verification, kana candidate characters recognized by the kana OCR are compared with kana`s searched from the name dictionary based on kanji name candidates, given by the word matching. The correct name is selected from the results of word matching and furigana verification. Also, the address approval efficiently searches for the right address based on a bottom-up procedure which follows hierarchical relations from a lower placename to a upper one by using the positional condition among the placenames. We ascertained that the error correcting method substantially improves the recognition rate and processing speed in experiments on 5,032 forms.« less

Handwritten character recognition using background analysis

NASA Astrophysics Data System (ADS)

Tascini, Guido; Puliti, Paolo; Zingaretti, Primo

1993-04-01

The paper describes a low-cost handwritten character recognizer. It is constituted by three modules: the `acquisition' module, the `binarization' module, and the `core' module. The core module can be logically partitioned into six steps: character dilation, character circumscription, region and `profile' analysis, `cut' analysis, decision tree descent, and result validation. Firstly, it reduces the resolution of the binarized regions and detects the minimum rectangle (MR) which encloses the character; the MR partitions the background into regions that surround the character or are enclosed by it, and allows it to define features as `profiles' and `cuts;' a `profile' is the set of vertical or horizontal minimum distances between a side of the MR and the character itself; a `cut' is a vertical or horizontal image segment delimited by the MR. Then, the core module classifies the character by descending along the decision tree on the basis of the analysis of regions around the character, in particular of the `profiles' and `cuts,' and without using context information. Finally, it recognizes the character or reactivates the core module by analyzing validation test results. The recognizer is largely insensible to character discontinuity and is able to detect Arabic numerals and English alphabet capital letters. The recognition rate of a 32 X 32 pixel character is of about 97% after the first iteration, and of over 98% after the second iteration.

Deep neural networks for texture classification-A theoretical analysis.

PubMed

Basu, Saikat; Mukhopadhyay, Supratik; Karki, Manohar; DiBiano, Robert; Ganguly, Sangram; Nemani, Ramakrishna; Gayaka, Shreekant

2018-01-01

We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Magnetic Tunnel Junction Based Long-Term Short-Term Stochastic Synapse for a Spiking Neural Network with On-Chip STDP Learning

NASA Astrophysics Data System (ADS)

Srinivasan, Gopalakrishnan; Sengupta, Abhronil; Roy, Kaushik

2016-07-01

Spiking Neural Networks (SNNs) have emerged as a powerful neuromorphic computing paradigm to carry out classification and recognition tasks. Nevertheless, the general purpose computing platforms and the custom hardware architectures implemented using standard CMOS technology, have been unable to rival the power efficiency of the human brain. Hence, there is a need for novel nanoelectronic devices that can efficiently model the neurons and synapses constituting an SNN. In this work, we propose a heterostructure composed of a Magnetic Tunnel Junction (MTJ) and a heavy metal as a stochastic binary synapse. Synaptic plasticity is achieved by the stochastic switching of the MTJ conductance states, based on the temporal correlation between the spiking activities of the interconnecting neurons. Additionally, we present a significance driven long-term short-term stochastic synapse comprising two unique binary synaptic elements, in order to improve the synaptic learning efficiency. We demonstrate the efficacy of the proposed synaptic configurations and the stochastic learning algorithm on an SNN trained to classify handwritten digits from the MNIST dataset, using a device to system-level simulation framework. The power efficiency of the proposed neuromorphic system stems from the ultra-low programming energy of the spintronic synapses.

Magnetic Tunnel Junction Based Long-Term Short-Term Stochastic Synapse for a Spiking Neural Network with On-Chip STDP Learning.

PubMed

Srinivasan, Gopalakrishnan; Sengupta, Abhronil; Roy, Kaushik

2016-07-13

Spiking Neural Networks (SNNs) have emerged as a powerful neuromorphic computing paradigm to carry out classification and recognition tasks. Nevertheless, the general purpose computing platforms and the custom hardware architectures implemented using standard CMOS technology, have been unable to rival the power efficiency of the human brain. Hence, there is a need for novel nanoelectronic devices that can efficiently model the neurons and synapses constituting an SNN. In this work, we propose a heterostructure composed of a Magnetic Tunnel Junction (MTJ) and a heavy metal as a stochastic binary synapse. Synaptic plasticity is achieved by the stochastic switching of the MTJ conductance states, based on the temporal correlation between the spiking activities of the interconnecting neurons. Additionally, we present a significance driven long-term short-term stochastic synapse comprising two unique binary synaptic elements, in order to improve the synaptic learning efficiency. We demonstrate the efficacy of the proposed synaptic configurations and the stochastic learning algorithm on an SNN trained to classify handwritten digits from the MNIST dataset, using a device to system-level simulation framework. The power efficiency of the proposed neuromorphic system stems from the ultra-low programming energy of the spintronic synapses.

Assessment of legibility and completeness of handwritten and electronic prescriptions.

PubMed

Albarrak, Ahmed I; Al Rashidi, Eman Abdulrahman; Fatani, Rwaa Kamil; Al Ageel, Shoog Ibrahim; Mohammed, Rafiuddin

2014-12-01

To assess the legibility and completeness of handwritten prescriptions and compare with electronic prescription system for medication errors. Prospective study. King Khalid University Hospital (KKUH), Riyadh, Saudi Arabia. Handwritten prescriptions were received from clinical units of Medicine Outpatient Department (MOPD), Primary Care Clinic (PCC) and Surgery Outpatient Department (SOPD) whereas electronic prescriptions were collected from the pediatric ward. The handwritten prescription was assessed for completeness by the checklist designed according to the hospital prescription and evaluated for legibility by two pharmacists. The comparison between handwritten and electronic prescription errors was evaluated based on the validated checklist adopted from previous studies. Legibility and completeness of prescriptions. 398 prescriptions (199 handwritten and 199 e-prescriptions) were assessed. About 71 (35.7%) of handwritten and 5 (2.5%) of electronic prescription errors were identified. A significant statistical difference (P < 0.001) was observed between handwritten and e-prescriptions in omitted dose and omitted route of administration category of error distribution. The rate of completeness in patient identification in handwritten prescriptions was 80.97% in MOPD, 76.36% in PCC and 85.93% in SOPD clinic units. Assessment of medication prescription completeness was 91.48% in MOPD, 88.48% in PCC, and 89.28% in SOPD. This study revealed a high incidence of prescribing errors in handwritten prescriptions. The use of e-prescription system showed a significant decline in the incidence of errors. The legibility of handwritten prescriptions was relatively good whereas the level of completeness was very low.

Digital Note-Taking: Discussion of Evidence and Best Practices.

PubMed

Grahame, Jason A

2016-03-01

Balancing active course engagement and comprehension with producing quality lecture notes is challenging. Although evidence suggests that handwritten note-taking may improve comprehension and learning outcomes, many students still self-report a preference for digital note-taking and a belief that it is beneficial. Future research is warranted to determine the effects on performance of digitally writing notes. Independent of the methods or software chosen, best practices should be provided to students with information to help them consciously make an educated decision based on the evidence and their personal preference. Optimal note-taking requires self-discipline, focused attention, sufficient working memory, thoughtful rewording, and decreased distractions. Familiarity with the tools and mediums they choose will help students maximize working memory, produce better notes, and aid in their retention of material presented.

Automated recognition and extraction of tabular fields for the indexing of census records

NASA Astrophysics Data System (ADS)

Clawson, Robert; Bauer, Kevin; Chidester, Glen; Pohontsch, Milan; Kennard, Douglas; Ryu, Jongha; Barrett, William

2013-01-01

We describe a system for indexing of census records in tabular documents with the goal of recognizing the content of each cell, including both headers and handwritten entries. Each document is automatically rectified, registered and scaled to a known template following which lines and fields are detected and delimited as cells in a tabular form. Whole-word or whole-phrase recognition of noisy machine-printed text is performed using a glyph library, providing greatly increased efficiency and accuracy (approaching 100%), while avoiding the problems inherent with traditional OCR approaches. Constrained handwriting recognition results for a single author reach as high as 98% and 94.5% for the Gender field and Birthplace respectively. Multi-author accuracy (currently 82%) can be improved through an increased training set. Active integration of user feedback in the system will accelerate the indexing of records while providing a tightly coupled learning mechanism for system improvement.

Shape analysis modeling for character recognition

NASA Astrophysics Data System (ADS)

Khan, Nadeem A. M.; Hegt, Hans A.

1998-10-01

Optimal shape modeling of character-classes is crucial for achieving high performance on recognition of mixed-font, hand-written or (and) poor quality text. A novel scheme is presented in this regard focusing on constructing such structural models that can be hierarchically examined. These models utilize a certain `well-thought' set of shape primitives. They are simplified enough to ignore the inter- class variations in font-type or writing style yet retaining enough details for discrimination between the samples of the similar classes. Thus the number of models per class required can be kept minimal without sacrificing the recognition accuracy. In this connection a flexible multi- stage matching scheme exploiting the proposed modeling is also described. This leads to a system which is robust against various distortions and degradation including those related to cases of touching and broken characters. Finally, we present some examples and test results as a proof-of- concept demonstrating the validity and the robustness of the approach.

A novel word spotting method based on recurrent neural networks.

PubMed

Frinken, Volkmar; Fischer, Andreas; Manmatha, R; Bunke, Horst

2012-02-01

Keyword spotting refers to the process of retrieving all instances of a given keyword from a document. In the present paper, a novel keyword spotting method for handwritten documents is described. It is derived from a neural network-based system for unconstrained handwriting recognition. As such it performs template-free spotting, i.e., it is not necessary for a keyword to appear in the training set. The keyword spotting is done using a modification of the CTC Token Passing algorithm in conjunction with a recurrent neural network. We demonstrate that the proposed systems outperform not only a classical dynamic time warping-based approach but also a modern keyword spotting system, based on hidden Markov models. Furthermore, we analyze the performance of the underlying neural networks when using them in a recognition task followed by keyword spotting on the produced transcription. We point out the advantages of keyword spotting when compared to classic text line recognition.

Enhancement and character recognition of the erased colophon of a 15th-century Hebrew prayer book

NASA Astrophysics Data System (ADS)

Walvoord, Derek J.; Easton, Roger L., Jr.; Knox, Keith T.; Heimbueger, Matthew

2005-01-01

A handwritten codex often included an inscription that listed facts about its publication, such as the names of the scribe and patron, date of publication, the city where the book was copied, etc. These facts obviously provide essential information to a historian studying the provenance of the codex. Unfortunately, this page was sometimes erased after the sale of the book to a new owner, often by scraping off the original ink. The importance of recovering this information would be difficult to overstate. This paper reports on the methods of imaging, image enhancement, and character recognition that were applied to this page in a Hebrew prayer book copied in Florence in the 15th century.

Enhancement and character recognition of the erased colophon of a 15th-century Hebrew prayer book

NASA Astrophysics Data System (ADS)

Walvoord, Derek J.; Easton, Roger L., Jr.; Knox, Keith T.; Heimbueger, Matthew

2004-12-01

A handwritten codex often included an inscription that listed facts about its publication, such as the names of the scribe and patron, date of publication, the city where the book was copied, etc. These facts obviously provide essential information to a historian studying the provenance of the codex. Unfortunately, this page was sometimes erased after the sale of the book to a new owner, often by scraping off the original ink. The importance of recovering this information would be difficult to overstate. This paper reports on the methods of imaging, image enhancement, and character recognition that were applied to this page in a Hebrew prayer book copied in Florence in the 15th century.

Introduction of statistical information in a syntactic analyzer for document image recognition

NASA Astrophysics Data System (ADS)

Maroneze, André O.; Coüasnon, Bertrand; Lemaitre, Aurélie

2011-01-01

This paper presents an improvement to document layout analysis systems, offering a possible solution to Sayre's paradox (which states that an element "must be recognized before it can be segmented; and it must be segmented before it can be recognized"). This improvement, based on stochastic parsing, allows integration of statistical information, obtained from recognizers, during syntactic layout analysis. We present how this fusion of numeric and symbolic information in a feedback loop can be applied to syntactic methods to improve document description expressiveness. To limit combinatorial explosion during exploration of solutions, we devised an operator that allows optional activation of the stochastic parsing mechanism. Our evaluation on 1250 handwritten business letters shows this method allows the improvement of global recognition scores.

Assessment of legibility and completeness of handwritten and electronic prescriptions

PubMed Central

Albarrak, Ahmed I; Al Rashidi, Eman Abdulrahman; Fatani, Rwaa Kamil; Al Ageel, Shoog Ibrahim; Mohammed, Rafiuddin

2014-01-01

Objectives To assess the legibility and completeness of handwritten prescriptions and compare with electronic prescription system for medication errors. Design Prospective study. Setting King Khalid University Hospital (KKUH), Riyadh, Saudi Arabia. Subjects and methods Handwritten prescriptions were received from clinical units of Medicine Outpatient Department (MOPD), Primary Care Clinic (PCC) and Surgery Outpatient Department (SOPD) whereas electronic prescriptions were collected from the pediatric ward. The handwritten prescription was assessed for completeness by the checklist designed according to the hospital prescription and evaluated for legibility by two pharmacists. The comparison between handwritten and electronic prescription errors was evaluated based on the validated checklist adopted from previous studies. Main outcome measures Legibility and completeness of prescriptions. Results 398 prescriptions (199 handwritten and 199 e-prescriptions) were assessed. About 71 (35.7%) of handwritten and 5 (2.5%) of electronic prescription errors were identified. A significant statistical difference (P < 0.001) was observed between handwritten and e-prescriptions in omitted dose and omitted route of administration category of error distribution. The rate of completeness in patient identification in handwritten prescriptions was 80.97% in MOPD, 76.36% in PCC and 85.93% in SOPD clinic units. Assessment of medication prescription completeness was 91.48% in MOPD, 88.48% in PCC, and 89.28% in SOPD. Conclusions This study revealed a high incidence of prescribing errors in handwritten prescriptions. The use of e-prescription system showed a significant decline in the incidence of errors. The legibility of handwritten prescriptions was relatively good whereas the level of completeness was very low. PMID:25561864

From Hahnemann's hand to your computer screen: building a digital homeopathy collection

PubMed Central

Mix, Lisa A; Cameron, Kathleen

2011-01-01

The University of California, San Francisco (UCSF), Library holds the unique manuscript of the sixth edition of Samuel Hahnemann's Organon der Heilkunst, the primary text of homeopathy. The manuscript volume is Hahnemann's own copy of the fifth edition of the Organon with his notes for the sixth edition, handwritten throughout the volume. There is a high level of interest in the Organon manuscript, particularly among homeopaths. This led to the decision to present a digital surrogate on the web to make it accessible to a wider audience. Digitizing Hahnemann's manuscript and determining the best method of presentation on the web posed several challenges. Lessons learned in the course of this project will inform future digital projects. This article discusses the historical significance of the sixth edition of Hahnemann's Organon, its context in UCSF's homeopathy collections, and the specifics of developing the online homeopathy collection. PMID:21243055

Basic test framework for the evaluation of text line segmentation and text parameter extraction.

PubMed

Brodić, Darko; Milivojević, Dragan R; Milivojević, Zoran

2010-01-01

Text line segmentation is an essential stage in off-line optical character recognition (OCR) systems. It is a key because inaccurately segmented text lines will lead to OCR failure. Text line segmentation of handwritten documents is a complex and diverse problem, complicated by the nature of handwriting. Hence, text line segmentation is a leading challenge in handwritten document image processing. Due to inconsistencies in measurement and evaluation of text segmentation algorithm quality, some basic set of measurement methods is required. Currently, there is no commonly accepted one and all algorithm evaluation is custom oriented. In this paper, a basic test framework for the evaluation of text feature extraction algorithms is proposed. This test framework consists of a few experiments primarily linked to text line segmentation, skew rate and reference text line evaluation. Although they are mutually independent, the results obtained are strongly cross linked. In the end, its suitability for different types of letters and languages as well as its adaptability are its main advantages. Thus, the paper presents an efficient evaluation method for text analysis algorithms.

Basic Test Framework for the Evaluation of Text Line Segmentation and Text Parameter Extraction

PubMed Central

Brodić, Darko; Milivojević, Dragan R.; Milivojević, Zoran

2010-01-01

Text line segmentation is an essential stage in off-line optical character recognition (OCR) systems. It is a key because inaccurately segmented text lines will lead to OCR failure. Text line segmentation of handwritten documents is a complex and diverse problem, complicated by the nature of handwriting. Hence, text line segmentation is a leading challenge in handwritten document image processing. Due to inconsistencies in measurement and evaluation of text segmentation algorithm quality, some basic set of measurement methods is required. Currently, there is no commonly accepted one and all algorithm evaluation is custom oriented. In this paper, a basic test framework for the evaluation of text feature extraction algorithms is proposed. This test framework consists of a few experiments primarily linked to text line segmentation, skew rate and reference text line evaluation. Although they are mutually independent, the results obtained are strongly cross linked. In the end, its suitability for different types of letters and languages as well as its adaptability are its main advantages. Thus, the paper presents an efficient evaluation method for text analysis algorithms. PMID:22399932

Handwritten text line segmentation by spectral clustering

NASA Astrophysics Data System (ADS)

Han, Xuecheng; Yao, Hui; Zhong, Guoqiang

2017-02-01

Since handwritten text lines are generally skewed and not obviously separated, text line segmentation of handwritten document images is still a challenging problem. In this paper, we propose a novel text line segmentation algorithm based on the spectral clustering. Given a handwritten document image, we convert it to a binary image first, and then compute the adjacent matrix of the pixel points. We apply spectral clustering on this similarity metric and use the orthogonal kmeans clustering algorithm to group the text lines. Experiments on Chinese handwritten documents database (HIT-MW) demonstrate the effectiveness of the proposed method.

A guide for digitising manuscript climate data

NASA Astrophysics Data System (ADS)

Brönnimann, S.; Annis, J.; Dann, W.; Ewen, T.; Grant, A. N.; Griesser, T.; Krähenmann, S.; Mohr, C.; Scherer, M.; Vogler, C.

2006-10-01

Hand-written or printed manuscript data are an important source for paleo-climatological studies, but bringing them into a suitable format can be a time consuming adventure with uncertain success. Before digitising such data (e.g., in the context a specific research project), it is worthwhile spending a few thoughts on the characteristics of the data, the scientific requirements with respect to quality and coverage, the metadata, and technical aspects such as reproduction techniques, digitising techniques, and quality control strategies. Here we briefly discuss the most important considerations according to our own experience and describe different methods for digitising numeric or text data (optical character recognition, speech recognition, and key entry). We present a tentative guide that is intended to help others compiling the necessary information and making the right decisions.

A guide for digitising manuscript climate data

NASA Astrophysics Data System (ADS)

Brönnimann, S.; Annis, J.; Dann, W.; Ewen, T.; Grant, A. N.; Griesser, T.; Krähenmann, S.; Mohr, C.; Scherer, M.; Vogler, C.

2006-05-01

Hand-written or printed manuscript data are an important source for paleo-climatological studies, but bringing them into a suitable format can be a time consuming adventure with uncertain success. Before starting the digitising work, it is worthwhile spending a few thoughts on the characteristics of the data, the scientific requirements with respect to quality and coverage, and on the different digitising techniques. Here we briefly discuss the most important considerations and report our own experience. We describe different methods for digitising numeric or text data, i.e., optical character recognition (OCR), speech recognition, and key entry. Each technique has its advantages and disadvantages that may become important for certain applications. It is therefore crucial to thoroughly investigate beforehand the characteristics of the manuscript data, define the quality targets and develop validation strategies.

Tensor Train Neighborhood Preserving Embedding

NASA Astrophysics Data System (ADS)

Wang, Wenqi; Aggarwal, Vaneet; Aeron, Shuchin

2018-05-01

In this paper, we propose a Tensor Train Neighborhood Preserving Embedding (TTNPE) to embed multi-dimensional tensor data into low dimensional tensor subspace. Novel approaches to solve the optimization problem in TTNPE are proposed. For this embedding, we evaluate novel trade-off gain among classification, computation, and dimensionality reduction (storage) for supervised learning. It is shown that compared to the state-of-the-arts tensor embedding methods, TTNPE achieves superior trade-off in classification, computation, and dimensionality reduction in MNIST handwritten digits and Weizmann face datasets.

Machine learning phases of matter

NASA Astrophysics Data System (ADS)

Carrasquilla, Juan; Stoudenmire, Miles; Melko, Roger

We show how the technology that allows automatic teller machines read hand-written digits in cheques can be used to encode and recognize phases of matter and phase transitions in many-body systems. In particular, we analyze the (quasi-)order-disorder transitions in the classical Ising and XY models. Furthermore, we successfully use machine learning to study classical Z2 gauge theories that have important technological application in the coming wave of quantum information technologies and whose phase transitions have no conventional order parameter.

Postprocessing for character recognition using pattern features and linguistic information

NASA Astrophysics Data System (ADS)

Yoshikawa, Takatoshi; Okamoto, Masayosi; Horii, Hiroshi

1993-04-01

We propose a new method of post-processing for character recognition using pattern features and linguistic information. This method corrects errors in the recognition of handwritten Japanese sentences containing Kanji characters. This post-process method is characterized by having two types of character recognition. Improving the accuracy of the character recognition rate of Japanese characters is made difficult by the large number of characters, and the existence of characters with similar patterns. Therefore, it is not practical for a character recognition system to recognize all characters in detail. First, this post-processing method generates a candidate character table by recognizing the simplest features of characters. Then, it selects words corresponding to the character from the candidate character table by referring to a word and grammar dictionary before selecting suitable words. If the correct character is included in the candidate character table, this process can correct an error, however, if the character is not included, it cannot correct an error. Therefore, if this method can presume a character does not exist in a candidate character table by using linguistic information (word and grammar dictionary). It then can verify a presumed character by character recognition using complex features. When this method is applied to an online character recognition system, the accuracy of character recognition improves 93.5% to 94.7%. This proved to be the case when it was used for the editorials of a Japanese newspaper (Asahi Shinbun).

Computerized Orders with Standardized Concentrations Decrease Dispensing Errors of Continuous Infusion Medications for Pediatrics

PubMed Central

Sowan, Azizeh K.; Vaidya, Vinay U.; Soeken, Karen L.; Hilmas, Elora

2010-01-01

OBJECTIVES The use of continuous infusion medications with individualized concentrations may increase the risk for errors in pediatric patients. The objective of this study was to evaluate the effect of computerized prescriber order entry (CPOE) for continuous infusions with standardized concentrations on frequency of pharmacy processing errors. In addition, time to process handwritten versus computerized infusion orders was evaluated and user satisfaction with CPOE as compared to handwritten orders was measured. METHODS Using a crossover design, 10 pharmacists in the pediatric satellite within a university teaching hospital were given test scenarios of handwritten and CPOE order sheets and asked to process infusion orders using the pharmacy system in order to generate infusion labels. Participants were given three groups of orders: five correct handwritten orders, four handwritten orders written with deliberate errors, and five correct CPOE orders. Label errors were analyzed and time to complete the task was recorded. RESULTS Using CPOE orders, participants required less processing time per infusion order (2 min, 5 sec ± 58 sec) compared with time per infusion order in the first handwritten order sheet group (3 min, 7 sec ± 1 min, 20 sec) and the second handwritten order sheet group (3 min, 26 sec ± 1 min, 8 sec), (p<0.01). CPOE eliminated all error types except wrong concentration. With CPOE, 4% of infusions processed contained errors, compared with 26% of the first group of handwritten orders and 45% of the second group of handwritten orders (p<0.03). Pharmacists were more satisfied with CPOE orders when compared with the handwritten method (p=0.0001). CONCLUSIONS CPOE orders saved pharmacists' time and greatly improved the safety of processing continuous infusions, although not all errors were eliminated. pharmacists were overwhelmingly satisfied with the CPOE orders PMID:22477811

All Spin Artificial Neural Networks Based on Compound Spintronic Synapse and Neuron.

PubMed

Zhang, Deming; Zeng, Lang; Cao, Kaihua; Wang, Mengxing; Peng, Shouzhong; Zhang, Yue; Zhang, Youguang; Klein, Jacques-Olivier; Wang, Yu; Zhao, Weisheng

2016-08-01

Artificial synaptic devices implemented by emerging post-CMOS non-volatile memory technologies such as Resistive RAM (RRAM) have made great progress recently. However, it is still a big challenge to fabricate stable and controllable multilevel RRAM. Benefitting from the control of electron spin instead of electron charge, spintronic devices, e.g., magnetic tunnel junction (MTJ) as a binary device, have been explored for neuromorphic computing with low power dissipation. In this paper, a compound spintronic device consisting of multiple vertically stacked MTJs is proposed to jointly behave as a synaptic device, termed as compound spintronic synapse (CSS). Based on our theoretical and experimental work, it has been demonstrated that the proposed compound spintronic device can achieve designable and stable multiple resistance states by interfacial and materials engineering of its components. Additionally, a compound spintronic neuron (CSN) circuit based on the proposed compound spintronic device is presented, enabling a multi-step transfer function. Then, an All Spin Artificial Neural Network (ASANN) is constructed with the CSS and CSN circuit. By conducting system-level simulations on the MNIST database for handwritten digital recognition, the performance of such ASANN has been investigated. Moreover, the impact of the resolution of both the CSS and CSN and device variation on the system performance are discussed in this work.

Fully parallel write/read in resistive synaptic array for accelerating on-chip learning

NASA Astrophysics Data System (ADS)

Gao, Ligang; Wang, I.-Ting; Chen, Pai-Yu; Vrudhula, Sarma; Seo, Jae-sun; Cao, Yu; Hou, Tuo-Hung; Yu, Shimeng

2015-11-01

A neuro-inspired computing paradigm beyond the von Neumann architecture is emerging and it generally takes advantage of massive parallelism and is aimed at complex tasks that involve intelligence and learning. The cross-point array architecture with synaptic devices has been proposed for on-chip implementation of the weighted sum and weight update in the learning algorithms. In this work, forming-free, silicon-process-compatible Ta/TaO x /TiO2/Ti synaptic devices are fabricated, in which >200 levels of conductance states could be continuously tuned by identical programming pulses. In order to demonstrate the advantages of parallelism of the cross-point array architecture, a novel fully parallel write scheme is designed and experimentally demonstrated in a small-scale crossbar array to accelerate the weight update in the training process, at a speed that is independent of the array size. Compared to the conventional row-by-row write scheme, it achieves >30× speed-up and >30× improvement in energy efficiency as projected in a large-scale array. If realistic synaptic device characteristics such as device variations are taken into an array-level simulation, the proposed array architecture is able to achieve ∼95% recognition accuracy of MNIST handwritten digits, which is close to the accuracy achieved by software using the ideal sparse coding algorithm.

A Set of Handwriting Features for Use in Automated Writer Identification.

PubMed

Miller, John J; Patterson, Robert Bradley; Gantz, Donald T; Saunders, Christopher P; Walch, Mark A; Buscaglia, JoAnn

2017-05-01

A writer's biometric identity can be characterized through the distribution of physical feature measurements ("writer's profile"); a graph-based system that facilitates the quantification of these features is described. To accomplish this quantification, handwriting is segmented into basic graphical forms ("graphemes"), which are "skeletonized" to yield the graphical topology of the handwritten segment. The graph-based matching algorithm compares the graphemes first by their graphical topology and then by their geometric features. Graphs derived from known writers can be compared against graphs extracted from unknown writings. The process is computationally intensive and relies heavily upon statistical pattern recognition algorithms. This article focuses on the quantification of these physical features and the construction of the associated pattern recognition methods for using the features to discriminate among writers. The graph-based system described in this article has been implemented in a highly accurate and approximately language-independent biometric recognition system of writers of cursive documents. © 2017 American Academy of Forensic Sciences.

Improving the delivery of care and reducing healthcare costs with the digitization of information.

PubMed

Noffsinger, R; Chin, S

2000-01-01

In the coming years, the digitization of information and the Internet will be extremely powerful in reducing healthcare costs while assisting providers in the delivery of care. One example of healthcare inefficiency that can be managed through information digitization is the process of prescription writing. Due to the handwritten and verbal communication surrounding prescription writing, as well as the multiple tiers of authorizations, the prescription drug process causes extensive financial waste as well as medical errors, lost time, and even fatal accidents. Electronic prescription management systems are being designed to address these inefficiencies. By utilizing new electronic prescription systems, physicians not only prescribe more accurately, but also improve formulary compliance thereby reducing pharmacy utilization. These systems expand patient care by presenting proactive alternatives at the point of prescription while reducing costs and providing additional benefits for consumers and healthcare providers.

A unified approach for development of Urdu Corpus for OCR and demographic purpose

NASA Astrophysics Data System (ADS)

Choudhary, Prakash; Nain, Neeta; Ahmed, Mushtaq

2015-02-01

This paper presents a methodology for the development of an Urdu handwritten text image Corpus and application of Corpus linguistics in the field of OCR and information retrieval from handwritten document. Compared to other language scripts, Urdu script is little bit complicated for data entry. To enter a single character it requires a combination of multiple keys entry. Here, a mixed approach is proposed and demonstrated for building Urdu Corpus for OCR and Demographic data collection. Demographic part of database could be used to train a system to fetch the data automatically, which will be helpful to simplify existing manual data-processing task involved in the field of data collection such as input forms like Passport, Ration Card, Voting Card, AADHAR, Driving licence, Indian Railway Reservation, Census data etc. This would increase the participation of Urdu language community in understanding and taking benefit of the Government schemes. To make availability and applicability of database in a vast area of corpus linguistics, we propose a methodology for data collection, mark-up, digital transcription, and XML metadata information for benchmarking.

Pen-chant: Acoustic emissions of handwriting and drawing

NASA Astrophysics Data System (ADS)

Seniuk, Andrew G.

The sounds generated by a writing instrument ('pen-chant') provide a rich and underutilized source of information for pattern recognition. We examine the feasibility of recognition of handwritten cursive text, exclusively through an analysis of acoustic emissions. We design and implement a family of recognizers using a template matching approach, with templates and similarity measures derived variously from: smoothed amplitude signal with fixed resolution, discrete sequence of magnitudes obtained from peaks in the smoothed amplitude signal, and ordered tree obtained from a scale space signal representation. Test results are presented for recognition of isolated lowercase cursive characters and for whole words. We also present qualitative results for recognizing gestures such as circling, scratch-out, check-marks, and hatching. Our first set of results, using samples provided by the author, yield recognition rates of over 70% (alphabet) and 90% (26 words), with a confidence of +/-8%, based solely on acoustic emissions. Our second set of results uses data gathered from nine writers. These results demonstrate that acoustic emissions are a rich source of information, usable---on their own or in conjunction with image-based features---to solve pattern recognition problems. In future work, this approach can be applied to writer identification, handwriting and gesture-based computer input technology, emotion recognition, and temporal analysis of sketches.

Recognition of Handwriting from Electromyography

PubMed Central

Linderman, Michael; Lebedev, Mikhail A.; Erlichman, Joseph S.

2009-01-01

Handwriting – one of the most important developments in human culture – is also a methodological tool in several scientific disciplines, most importantly handwriting recognition methods, graphology and medical diagnostics. Previous studies have relied largely on the analyses of handwritten traces or kinematic analysis of handwriting; whereas electromyographic (EMG) signals associated with handwriting have received little attention. Here we show for the first time, a method in which EMG signals generated by hand and forearm muscles during handwriting activity are reliably translated into both algorithm-generated handwriting traces and font characters using decoding algorithms. Our results demonstrate the feasibility of recreating handwriting solely from EMG signals – the finding that can be utilized in computer peripherals and myoelectric prosthetic devices. Moreover, this approach may provide a rapid and sensitive method for diagnosing a variety of neurogenerative diseases before other symptoms become clear. PMID:19707562

Performance evaluation methodology for historical document image binarization.

PubMed

Ntirogiannis, Konstantinos; Gatos, Basilis; Pratikakis, Ioannis

2013-02-01

Document image binarization is of great importance in the document image analysis and recognition pipeline since it affects further stages of the recognition process. The evaluation of a binarization method aids in studying its algorithmic behavior, as well as verifying its effectiveness, by providing qualitative and quantitative indication of its performance. This paper addresses a pixel-based binarization evaluation methodology for historical handwritten/machine-printed document images. In the proposed evaluation scheme, the recall and precision evaluation measures are properly modified using a weighting scheme that diminishes any potential evaluation bias. Additional performance metrics of the proposed evaluation scheme consist of the percentage rates of broken and missed text, false alarms, background noise, character enlargement, and merging. Several experiments conducted in comparison with other pixel-based evaluation measures demonstrate the validity of the proposed evaluation scheme.

Reducing weight precision of convolutional neural networks towards large-scale on-chip image recognition

NASA Astrophysics Data System (ADS)

Ji, Zhengping; Ovsiannikov, Ilia; Wang, Yibing; Shi, Lilong; Zhang, Qiang

2015-05-01

In this paper, we develop a server-client quantization scheme to reduce bit resolution of deep learning architecture, i.e., Convolutional Neural Networks, for image recognition tasks. Low bit resolution is an important factor in bringing the deep learning neural network into hardware implementation, which directly determines the cost and power consumption. We aim to reduce the bit resolution of the network without sacrificing its performance. To this end, we design a new quantization algorithm called supervised iterative quantization to reduce the bit resolution of learned network weights. In the training stage, the supervised iterative quantization is conducted via two steps on server - apply k-means based adaptive quantization on learned network weights and retrain the network based on quantized weights. These two steps are alternated until the convergence criterion is met. In this testing stage, the network configuration and low-bit weights are loaded to the client hardware device to recognize coming input in real time, where optimized but expensive quantization becomes infeasible. Considering this, we adopt a uniform quantization for the inputs and internal network responses (called feature maps) to maintain low on-chip expenses. The Convolutional Neural Network with reduced weight and input/response precision is demonstrated in recognizing two types of images: one is hand-written digit images and the other is real-life images in office scenarios. Both results show that the new network is able to achieve the performance of the neural network with full bit resolution, even though in the new network the bit resolution of both weight and input are significantly reduced, e.g., from 64 bits to 4-5 bits.

Maximum entropy PDF projection: A review

NASA Astrophysics Data System (ADS)

Baggenstoss, Paul M.

2017-06-01

We review maximum entropy (MaxEnt) PDF projection, a method with wide potential applications in statistical inference. The method constructs a sampling distribution for a high-dimensional vector x based on knowing the sampling distribution p(z) of a lower-dimensional feature z = T (x). Under mild conditions, the distribution p(x) having highest possible entropy among all distributions consistent with p(z) may be readily found. Furthermore, the MaxEnt p(x) may be sampled, making the approach useful in Monte Carlo methods. We review the theorem and present a case study in model order selection and classification for handwritten character recognition.

Two-stage approach to keyword spotting in handwritten documents

NASA Astrophysics Data System (ADS)

Haji, Mehdi; Ameri, Mohammad R.; Bui, Tien D.; Suen, Ching Y.; Ponson, Dominique

2013-12-01

Separation of keywords from non-keywords is the main problem in keyword spotting systems which has traditionally been approached by simplistic methods, such as thresholding of recognition scores. In this paper, we analyze this problem from a machine learning perspective, and we study several standard machine learning algorithms specifically in the context of non-keyword rejection. We propose a two-stage approach to keyword spotting and provide a theoretical analysis of the performance of the system which gives insights on how to design the classifier in order to maximize the overall performance in terms of F-measure.

Invariant approach to the character classification

NASA Astrophysics Data System (ADS)

Šariri, Kristina; Demoli, Nazif

2008-04-01

Image moments analysis is a very useful tool which allows image description invariant to translation and rotation, scale change and some types of image distortions. The aim of this work was development of simple method for fast and reliable classification of characters by using Hu's and affine moment invariants. Measure of Eucleidean distance was used as a discrimination feature with statistical parameters estimated. The method was tested in classification of Times New Roman font letters as well as sets of the handwritten characters. It is shown that using all Hu's and three affine invariants as discrimination set improves recognition rate by 30%.

Hemispheric Differences in Processing Handwritten Cursive

ERIC Educational Resources Information Center

Hellige, Joseph B.; Adamson, Maheen M.

2007-01-01

Hemispheric asymmetry was examined for native English speakers identifying consonant-vowel-consonant (CVC) non-words presented in standard printed form, in standard handwritten cursive form or in handwritten cursive with the letters separated by small gaps. For all three conditions, fewer errors occurred when stimuli were presented to the right…

Unsupervised categorization method of graphemes on handwritten manuscripts: application to style recognition

NASA Astrophysics Data System (ADS)

Daher, H.; Gaceb, D.; Eglin, V.; Bres, S.; Vincent, N.

2012-01-01

We present in this paper a feature selection and weighting method for medieval handwriting images that relies on codebooks of shapes of small strokes of characters (graphemes that are issued from the decomposition of manuscripts). These codebooks are important to simplify the automation of the analysis, the manuscripts transcription and the recognition of styles or writers. Our approach provides a precise features weighting by genetic algorithms and a highperformance methodology for the categorization of the shapes of graphemes by using graph coloring into codebooks which are applied in turn on CBIR (Content Based Image Retrieval) in a mixed handwriting database containing different pages from different writers, periods of the history and quality. We show how the coupling of these two mechanisms 'features weighting - graphemes classification' can offer a better separation of the forms to be categorized by exploiting their grapho-morphological, their density and their significant orientations particularities.

Usage of the back-propagation method for alphabet recognition

NASA Astrophysics Data System (ADS)

Shaila Sree, R. N.; Eswaran, Kumar; Sundararajan, N.

1999-03-01

Artificial Neural Networks play a pivotal role in the branch of Artificial Intelligence. They can be trained efficiently for a variety of tasks using different methods, of which the Back Propagation method is one among them. The paper studies the choosing of various design parameters of a neural network for the Back Propagation method. The study shows that when these parameters are properly assigned, the training task of the net is greatly simplified. The character recognition problem has been chosen as a test case for this study. A sample space of different handwritten characters of the English alphabet was gathered. A Neural net is finally designed taking many the design aspects into consideration and trained for different styles of writing. Experimental results are reported and discussed. It has been found that an appropriate choice of the design parameters of the neural net for the Back Propagation method reduces the training time and improves the performance of the net.

A deep belief network with PLSR for nonlinear system modeling.

PubMed

Qiao, Junfei; Wang, Gongming; Li, Wenjing; Li, Xiaoli

2018-08-01

Nonlinear system modeling plays an important role in practical engineering, and deep learning-based deep belief network (DBN) is now popular in nonlinear system modeling and identification because of the strong learning ability. However, the existing weights optimization for DBN is based on gradient, which always leads to a local optimum and a poor training result. In this paper, a DBN with partial least square regression (PLSR-DBN) is proposed for nonlinear system modeling, which focuses on the problem of weights optimization for DBN using PLSR. Firstly, unsupervised contrastive divergence (CD) algorithm is used in weights initialization. Secondly, initial weights derived from CD algorithm are optimized through layer-by-layer PLSR modeling from top layer to bottom layer. Instead of gradient method, PLSR-DBN can determine the optimal weights using several PLSR models, so that a better performance of PLSR-DBN is achieved. Then, the analysis of convergence is theoretically given to guarantee the effectiveness of the proposed PLSR-DBN model. Finally, the proposed PLSR-DBN is tested on two benchmark nonlinear systems and an actual wastewater treatment system as well as a handwritten digit recognition (nonlinear mapping and modeling) with high-dimension input data. The experiment results show that the proposed PLSR-DBN has better performances of time and accuracy on nonlinear system modeling than that of other methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Impact of Linearity and Write Noise of Analog Resistive Memory Devices in a Neural Algorithm Accelerator

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jacobs-Gedrim, Robin B.; Agarwal, Sapan; Knisely, Kathrine E.

Resistive memory (ReRAM) shows promise for use as an analog synapse element in energy-efficient neural network algorithm accelerators. A particularly important application is the training of neural networks, as this is the most computationally-intensive procedure in using a neural algorithm. However, training a network with analog ReRAM synapses can significantly reduce the accuracy at the algorithm level. In order to assess this degradation, analog properties of ReRAM devices were measured and hand-written digit recognition accuracy was modeled for the training using backpropagation. Bipolar filamentary devices utilizing three material systems were measured and compared: one oxygen vacancy system, Ta-TaO x, andmore » two conducting metallization systems, Cu-SiO 2, and Ag/chalcogenide. Analog properties and conductance ranges of the devices are optimized by measuring the response to varying voltage pulse characteristics. Key analog device properties which degrade the accuracy are update linearity and write noise. Write noise may improve as a function of device manufacturing maturity, but write nonlinearity appears relatively consistent among the different device material systems and is found to be the most significant factor affecting accuracy. As a result, this suggests that new materials and/or fundamentally different resistive switching mechanisms may be required to improve device linearity and achieve higher algorithm training accuracy.« less

Impact of Linearity and Write Noise of Analog Resistive Memory Devices in a Neural Algorithm Accelerator

DOE PAGES

Jacobs-Gedrim, Robin B.; Agarwal, Sapan; Knisely, Kathrine E.; ...

2017-12-01

Resistive memory (ReRAM) shows promise for use as an analog synapse element in energy-efficient neural network algorithm accelerators. A particularly important application is the training of neural networks, as this is the most computationally-intensive procedure in using a neural algorithm. However, training a network with analog ReRAM synapses can significantly reduce the accuracy at the algorithm level. In order to assess this degradation, analog properties of ReRAM devices were measured and hand-written digit recognition accuracy was modeled for the training using backpropagation. Bipolar filamentary devices utilizing three material systems were measured and compared: one oxygen vacancy system, Ta-TaO x, andmore » two conducting metallization systems, Cu-SiO 2, and Ag/chalcogenide. Analog properties and conductance ranges of the devices are optimized by measuring the response to varying voltage pulse characteristics. Key analog device properties which degrade the accuracy are update linearity and write noise. Write noise may improve as a function of device manufacturing maturity, but write nonlinearity appears relatively consistent among the different device material systems and is found to be the most significant factor affecting accuracy. As a result, this suggests that new materials and/or fundamentally different resistive switching mechanisms may be required to improve device linearity and achieve higher algorithm training accuracy.« less

Handwritten word preprocessing for database adaptation

NASA Astrophysics Data System (ADS)

Oprean, Cristina; Likforman-Sulem, Laurence; Mokbel, Chafic

2013-01-01

Handwriting recognition systems are typically trained using publicly available databases, where data have been collected in controlled conditions (image resolution, paper background, noise level,...). Since this is not often the case in real-world scenarios, classification performance can be affected when novel data is presented to the word recognition system. To overcome this problem, we present in this paper a new approach called database adaptation. It consists of processing one set (training or test) in order to adapt it to the other set (test or training, respectively). Specifically, two kinds of preprocessing, namely stroke thickness normalization and pixel intensity normalization are considered. The advantage of such approach is that we can re-use the existing recognition system trained on controlled data. We conduct several experiments with the Rimes 2011 word database and with a real-world database. We adapt either the test set or the training set. Results show that training set adaptation achieves better results than test set adaptation, at the cost of a second training stage on the adapted data. Accuracy of data set adaptation is increased by 2% to 3% in absolute value over no adaptation.

Unsupervised Word Spotting in Historical Handwritten Document Images using Document-oriented Local Features.

PubMed

Zagoris, Konstantinos; Pratikakis, Ioannis; Gatos, Basilis

2017-05-03

Word spotting strategies employed in historical handwritten documents face many challenges due to variation in the writing style and intense degradation. In this paper, a new method that permits effective word spotting in handwritten documents is presented that it relies upon document-oriented local features which take into account information around representative keypoints as well a matching process that incorporates spatial context in a local proximity search without using any training data. Experimental results on four historical handwritten datasets for two different scenarios (segmentation-based and segmentation-free) using standard evaluation measures show the improved performance achieved by the proposed methodology.

Exploring the use of tablet PCs in veterinary medical education: opportunity or obstacle?

PubMed

Wang, Hong; Rush, Bonnie R; Wilkerson, Melinda; van der Merwe, Deon

2014-01-01

A tablet PC is a laptop computer with a touch screen and a digital pen or stylus that can be used for handwritten notes and drawings. The use of tablet PCs has been investigated in many disciplines such as engineering, mathematics, science, and education. The purpose of this article is to explore student and faculty attitudes toward and experiences with tablet PCs 6 years after the implementation of a tablet PC program in the College of Veterinary Medicine (CVM) at Kansas State University (K-State). This study reports that the use of tablet PCs has enhanced students' learning experiences through learner-interface interaction, learner-content interaction, learner-instructor interaction, and learner-learner interaction. This study also identifies digital distraction as the major negative experience with tablet PCs during class time. The tablet PC program provides CVM faculty the potential to pursue technology integration strategies that support expected learning outcomes and provides students the potential to develop self-monitoring and self-discipline skills that support learning with digital technologies.

Research on Signature Verification Method Based on Discrete Fréchet Distance

NASA Astrophysics Data System (ADS)

Fang, J. L.; Wu, W.

2018-05-01

This paper proposes a multi-feature signature template based on discrete Fréchet distance, which breaks through the limitation of traditional signature authentication using a single signature feature. It solves the online handwritten signature authentication signature global feature template extraction calculation workload, signature feature selection unreasonable problem. In this experiment, the false recognition rate (FAR) and false rejection rate (FRR) of the statistical signature are calculated and the average equal error rate (AEER) is calculated. The feasibility of the combined template scheme is verified by comparing the average equal error rate of the combination template and the original template.

Dense modifiable interconnections utilizing photorefractive volume holograms

NASA Astrophysics Data System (ADS)

Psaltis, Demetri; Qiao, Yong

1990-11-01

This report describes an experimental two-layer optical neural network built at Caltech. The system uses photorefractive volume holograms to implement dense, modifiable synaptic interconnections and liquid crystal light valves (LCVS) to perform nonlinear thresholding operations. Kanerva's Sparse, Distributed Memory was implemented using this network and its ability to recognize handwritten character-alphabet (A-Z) has been demonstrated experimentally. According to Kanerva's model, the first layer has fixed, random weights of interconnections and the second layer is trained by sum-of-outer-products rule. After training, the recognition rates of the network on the training set (104 patterns) and test set (520 patterns) are 100 and 50 percent, respectively.

The data life cycle applied to our own data.

PubMed

Goben, Abigail; Raszewski, Rebecca

2015-01-01

Increased demand for data-driven decision making is driving the need for librarians to be facile with the data life cycle. This case study follows the migration of reference desk statistics from handwritten to digital format. This shift presented two opportunities: first, the availability of a nonsensitive data set to improve the librarians' understanding of data-management and statistical analysis skills, and second, the use of analytics to directly inform staffing decisions and departmental strategic goals. By working through each step of the data life cycle, library faculty explored data gathering, storage, sharing, and analysis questions.

Line Segmentation in Handwritten Assamese and Meetei Mayek Script Using Seam Carving Based Algorithm

NASA Astrophysics Data System (ADS)

Kumar, Chandan Jyoti; Kalita, Sanjib Kr.

Line segmentation is a key stage in an Optical Character Recognition system. This paper primarily concerns the problem of text line extraction on color and grayscale manuscript pages of two major North-east Indian regional Scripts, Assamese and Meetei Mayek. Line segmentation of handwritten text in Assamese and Meetei Mayek scripts is an uphill task primarily because of the structural features of both the scripts and varied writing styles. Line segmentation of a document image is been achieved by using the Seam carving technique, in this paper. Researchers from various regions used this approach for content aware resizing of an image. However currently many researchers are implementing Seam Carving for line segmentation phase of OCR. Although it is a language independent technique, mostly experiments are done over Arabic, Greek, German and Chinese scripts. Two types of seams are generated, medial seams approximate the orientation of each text line, and separating seams separated one line of text from another. Experiments are performed extensively over various types of documents and detailed analysis of the evaluations reflects that the algorithm performs well for even documents with multiple scripts. In this paper, we present a comparative study of accuracy of this method over different types of data.

Digital signal processing algorithms for automatic voice recognition

NASA Technical Reports Server (NTRS)

Botros, Nazeih M.

1987-01-01

The current digital signal analysis algorithms are investigated that are implemented in automatic voice recognition algorithms. Automatic voice recognition means, the capability of a computer to recognize and interact with verbal commands. The digital signal is focused on, rather than the linguistic, analysis of speech signal. Several digital signal processing algorithms are available for voice recognition. Some of these algorithms are: Linear Predictive Coding (LPC), Short-time Fourier Analysis, and Cepstrum Analysis. Among these algorithms, the LPC is the most widely used. This algorithm has short execution time and do not require large memory storage. However, it has several limitations due to the assumptions used to develop it. The other 2 algorithms are frequency domain algorithms with not many assumptions, but they are not widely implemented or investigated. However, with the recent advances in the digital technology, namely signal processors, these 2 frequency domain algorithms may be investigated in order to implement them in voice recognition. This research is concerned with real time, microprocessor based recognition algorithms.

The Need for Careful Data Collection for Pattern Recognition in Digital Pathology.

PubMed

Marée, Raphaël

2017-01-01

Effective pattern recognition requires carefully designed ground-truth datasets. In this technical note, we first summarize potential data collection issues in digital pathology and then propose guidelines to build more realistic ground-truth datasets and to control their quality. We hope our comments will foster the effective application of pattern recognition approaches in digital pathology.

Sensitivity and specificity of a digit symbol recognition trial in the identification of response bias.

PubMed

Kim, Nancy; Boone, Kyle B; Victor, Tara; Lu, Po; Keatinge, Carolyn; Mitchell, Cary

2010-08-01

Recently published practice standards recommend that multiple effort indicators be interspersed throughout neuropsychological evaluations to assess for response bias, which is most efficiently accomplished through use of effort indicators from standard cognitive tests already included in test batteries. The present study examined the utility of a timed recognition trial added to standard administration of the WAIS-III Digit Symbol subtest in a large sample of "real world" noncredible patients (n=82) as compared with credible neuropsychology clinic patients (n=89). Scores from the recognition trial were more sensitive in identifying poor effort than were standard Digit Symbol scores, and use of an equation incorporating Digit Symbol Age-Corrected Scaled Scores plus accuracy and time scores from the recognition trial was associated with nearly 80% sensitivity at 88.7% specificity. Thus, inclusion of a brief recognition trial to Digit Symbol administration has the potential to provide accurate assessment of response bias.

Correlation of patient entry rates and physician documentation errors in dictated and handwritten emergency treatment records.

PubMed

Dawdy, M R; Munter, D W; Gilmore, R A

1997-03-01

This study was designed to examine the relationship between patient entry rates (a measure of physician work load) and documentation errors/omissions in both handwritten and dictated emergency treatment records. The study was carried out in two phases. Phase I examined handwritten records and Phase II examined dictated and transcribed records. A total of 838 charts for three common chief complaints (chest pain, abdominal pain, asthma/chronic obstructive pulmonary disease) were retrospectively reviewed and scored for the presence or absence of 11 predetermined criteria. Patient entry rates were determined by reviewing the emergency department patient registration logs. The data were analyzed using simple correlation and linear regression analysis. A positive correlation was found between patient entry rates and documentation errors in handwritten charts. No such correlation was found in the dictated charts. We conclude that work load may negatively affect documentation accuracy when charts are handwritten. However, the use of dictation services may minimize or eliminate this effect.

Completion of hand-written surgical consent forms is frequently suboptimal and could be improved by using electronically generated, procedure-specific forms.

PubMed

St John, E R; Scott, A J; Irvine, T E; Pakzad, F; Leff, D R; Layer, G T

2017-08-01

Completion of hand-written consent forms for surgical procedures may suffer from missing or inaccurate information, poor legibility and high variability. We audited the completion of hand-written consent forms and trialled a web-based application to generate modifiable, procedure-specific consent forms. The investigation comprised two phases at separate UK hospitals. In phase one, the completion of individual responses in hand-written consent forms for a variety of procedures were prospectively audited. Responses were categorised into three domains (patient details, procedure details and patient sign-off) that were considered "failed" if a contained element was not correct and legible. Phase two was confined to a breast surgical unit where hand-written consent forms were assessed as for phase one and interrogated for missing complications by two independent experts. An electronic consent platform was introduced and electronically-produced consent forms assessed. In phase one, 99 hand-written consent forms were assessed and the domain failure rates were: patient details 10%; procedure details 30%; and patient sign-off 27%. Laparoscopic cholecystectomy was the most common procedure (7/99) but there was significant variability in the documentation of complications: 12 in total, a median of 6 and a range of 2-9. In phase two, 44% (27/61) of hand-written forms were missing essential complications. There were no domain failures amongst 29 electronically-produced consent forms and no variability in the documentation of potential complications. Completion of hand-written consent forms suffers from wide variation and is frequently suboptimal. Electronically-produced, procedure-specific consent forms can improve the quality and consistency of consent documentation. Copyright © 2015 Royal College of Surgeons of Edinburgh (Scottish charity number SC005317) and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. All rights reserved.

Physical Principles of the Method for Determination of Geometrical Characteristics and Particle Recognition in Digital Holography

NASA Astrophysics Data System (ADS)

Dyomin, V. V.; Polovtsev, I. G.; Davydova, A. Yu.

2018-03-01

The physical principles of a method for determination of geometrical characteristics of particles and particle recognition based on the concepts of digital holography, followed by processing of the particle images reconstructed from the digital hologram, using the morphological parameter are reported. An example of application of this method for fast plankton particle recognition is given.

Automatic Mexican sign language and digits recognition using normalized central moments

NASA Astrophysics Data System (ADS)

Solís, Francisco; Martínez, David; Espinosa, Oscar; Toxqui, Carina

2016-09-01

This work presents a framework for automatic Mexican sign language and digits recognition based on computer vision system using normalized central moments and artificial neural networks. Images are captured by digital IP camera, four LED reflectors and a green background in order to reduce computational costs and prevent the use of special gloves. 42 normalized central moments are computed per frame and used in a Multi-Layer Perceptron to recognize each database. Four versions per sign and digit were used in training phase. 93% and 95% of recognition rates were achieved for Mexican sign language and digits respectively.

Reduction in chemotherapy order errors with computerized physician order entry.

PubMed

Meisenberg, Barry R; Wright, Robert R; Brady-Copertino, Catherine J

2014-01-01

To measure the number and type of errors associated with chemotherapy order composition associated with three sequential methods of ordering: handwritten orders, preprinted orders, and computerized physician order entry (CPOE) embedded in the electronic health record. From 2008 to 2012, a sample of completed chemotherapy orders were reviewed by a pharmacist for the number and type of errors as part of routine performance improvement monitoring. Error frequencies for each of the three distinct methods of composing chemotherapy orders were compared using statistical methods. The rate of problematic order sets-those requiring significant rework for clarification-was reduced from 30.6% with handwritten orders to 12.6% with preprinted orders (preprinted v handwritten, P < .001) to 2.2% with CPOE (preprinted v CPOE, P < .001). The incidence of errors capable of causing harm was reduced from 4.2% with handwritten orders to 1.5% with preprinted orders (preprinted v handwritten, P < .001) to 0.1% with CPOE (CPOE v preprinted, P < .001). The number of problem- and error-containing chemotherapy orders was reduced sequentially by preprinted order sets and then by CPOE. CPOE is associated with low error rates, but it did not eliminate all errors, and the technology can introduce novel types of errors not seen with traditional handwritten or preprinted orders. Vigilance even with CPOE is still required to avoid patient harm.

Image distortion analysis using polynomial series expansion.

PubMed

Baggenstoss, Paul M

2004-11-01

In this paper, we derive a technique for analysis of local distortions which affect data in real-world applications. In the paper, we focus on image data, specifically handwritten characters. Given a reference image and a distorted copy of it, the method is able to efficiently determine the rotations, translations, scaling, and any other distortions that have been applied. Because the method is robust, it is also able to estimate distortions for two unrelated images, thus determining the distortions that would be required to cause the two images to resemble each other. The approach is based on a polynomial series expansion using matrix powers of linear transformation matrices. The technique has applications in pattern recognition in the presence of distortions.

Recognizing characters of ancient manuscripts

NASA Astrophysics Data System (ADS)

Diem, Markus; Sablatnig, Robert

2010-02-01

Considering printed Latin text, the main issues of Optical Character Recognition (OCR) systems are solved. However, for degraded handwritten document images, basic preprocessing steps such as binarization, gain poor results with state-of-the-art methods. In this paper ancient Slavonic manuscripts from the 11th century are investigated. In order to minimize the consequences of false character segmentation, a binarization-free approach based on local descriptors is proposed. Additionally local information allows the recognition of partially visible or washed out characters. The proposed algorithm consists of two steps: character classification and character localization. Initially Scale Invariant Feature Transform (SIFT) features are extracted which are subsequently classified using Support Vector Machines (SVM). Afterwards, the interest points are clustered according to their spatial information. Thereby, characters are localized and finally recognized based on a weighted voting scheme of pre-classified local descriptors. Preliminary results show that the proposed system can handle highly degraded manuscript images with background clutter (e.g. stains, tears) and faded out characters.

Parameter calibration for synthesizing realistic-looking variability in offline handwriting

NASA Astrophysics Data System (ADS)

Cheng, Wen; Lopresti, Dan

2011-01-01

Motivated by the widely accepted principle that the more training data, the better a recognition system performs, we conducted experiments asking human subjects to do evaluate a mixture of real English handwritten text lines and text lines altered from existing handwriting with various distortion degrees. The idea of generating synthetic handwriting is based on a perturbation method by T. Varga and H. Bunke that distorts an entire text line. There are two purposes of our experiments. First, we want to calibrate distortion parameter settings for Varga and Bunke's perturbation model. Second, we intend to compare the effects of parameter settings on different writing styles: block, cursive and mixed. From the preliminary experimental results, we determined appropriate ranges for parameter amplitude, and found that parameter settings should be altered for different handwriting styles. With the proper parameter settings, it should be possible to generate large amount of training and testing data for building better off-line handwriting recognition systems.

Eye movement analysis for activity recognition using electrooculography.

PubMed

Bulling, Andreas; Ward, Jamie A; Gellersen, Hans; Tröster, Gerhard

2011-04-01

In this work, we investigate eye movement analysis as a new sensing modality for activity recognition. Eye movement data were recorded using an electrooculography (EOG) system. We first describe and evaluate algorithms for detecting three eye movement characteristics from EOG signals-saccades, fixations, and blinks-and propose a method for assessing repetitive patterns of eye movements. We then devise 90 different features based on these characteristics and select a subset of them using minimum redundancy maximum relevance (mRMR) feature selection. We validate the method using an eight participant study in an office environment using an example set of five activity classes: copying a text, reading a printed paper, taking handwritten notes, watching a video, and browsing the Web. We also include periods with no specific activity (the NULL class). Using a support vector machine (SVM) classifier and person-independent (leave-one-person-out) training, we obtain an average precision of 76.1 percent and recall of 70.5 percent over all classes and participants. The work demonstrates the promise of eye-based activity recognition (EAR) and opens up discussion on the wider applicability of EAR to other activities that are difficult, or even impossible, to detect using common sensing modalities.

Event-driven contrastive divergence for spiking neuromorphic systems.

PubMed

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2013-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.

Event-driven contrastive divergence for spiking neuromorphic systems

PubMed Central

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2014-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality. PMID:24574952

Newly Digitized Historical Climate Data of the German Bight and the Southern Baltic Sea Coasts

NASA Astrophysics Data System (ADS)

Röhrbein, Dörte; Tinz, Birger; von Storch, Hans

2015-04-01

The detection of historical climate information plays an important role with regard to the discussion on climate change, particularly on storminess. The German Meteorological Service houses huge archives of historical handwritten journals of weather observations. A considerable number of original observation sheets from stations along the coast of the German Bight and the southern Baltic Sea exists which has been until recently almost unnoticed. These stations are called signal stations and are positioned close to the shore. However, for this region meteorological observation data of 128 stations exist from 1877 to 1999 and are partly digitized. In this study we show an analysis of firstly newly digitized wind and surface air pressure data of 15 stations from 1877 to 1939 and we also present a case study of the storm surge at the coast of the southern Baltic Sea in December 1913. The data are quality controlled by formal, climatological, temporal and consistency checks. It is shown that these historical climate data are usable in consistency and quality for further investigations on climate change, e.g. as input for regional and global reanalysis.

The Fisher-Markov selector: fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data.

PubMed

Cheng, Qiang; Zhou, Hongbo; Cheng, Jie

2011-06-01

Selecting features for multiclass classification is a critically important task for pattern recognition and machine learning applications. Especially challenging is selecting an optimal subset of features from high-dimensional data, which typically have many more variables than observations and contain significant noise, missing components, or outliers. Existing methods either cannot handle high-dimensional data efficiently or scalably, or can only obtain local optimum instead of global optimum. Toward the selection of the globally optimal subset of features efficiently, we introduce a new selector--which we call the Fisher-Markov selector--to identify those features that are the most useful in describing essential differences among the possible groups. In particular, in this paper we present a way to represent essential discriminating characteristics together with the sparsity as an optimization objective. With properly identified measures for the sparseness and discriminativeness in possibly high-dimensional settings, we take a systematic approach for optimizing the measures to choose the best feature subset. We use Markov random field optimization techniques to solve the formulated objective functions for simultaneous feature selection. Our results are noncombinatorial, and they can achieve the exact global optimum of the objective function for some special kernels. The method is fast; in particular, it can be linear in the number of features and quadratic in the number of observations. We apply our procedure to a variety of real-world data, including mid--dimensional optical handwritten digit data set and high-dimensional microarray gene expression data sets. The effectiveness of our method is confirmed by experimental results. In pattern recognition and from a model selection viewpoint, our procedure says that it is possible to select the most discriminating subset of variables by solving a very simple unconstrained objective function which in fact can be obtained with an explicit expression.

A comparison of the surface contaminants of handwritten recycled and printed electronic parenteral nutrition prescriptions and their transfer to bag surfaces during delivery to hospital wards.

PubMed

Austin, Peter David; Hand, Kieran Sean; Elia, Marinos

2014-02-01

Handwritten recycled paper prescription for parenteral nutrition (PN) may become a concentrated source of viable contaminants, including pathogens. This study examined the effect of using fresh printouts of electronic prescriptions on these contaminants. Cellulose sponge stick swabs with neutralizing buffer were used to sample the surfaces of PN prescriptions (n = 32 handwritten recycled; n = 32 printed electronic) on arrival to the pharmacy or following printing and PN prescriptions and bags packaged together during delivery (n = 38 handwritten recycled; n = 34 printed electronic) on arrival to hospital wards. Different media plates and standard microbiological procedures identified the type and number of contaminants. Staphylococcus aureus, fungi, and mold were infrequent contaminants. nonspecific aerobes more frequently contaminated handwritten recycled than printed electronic prescriptions (into pharmacy, 94% vs 44%, fisher exact test P .001; onto wards, 76% vs 50%, p = .028), with greater numbers of colony-forming units (CFU) (into pharmacy, median 130 [interquartile range (IQR), 65260] VS 0 [075], Mann-Whitney U test, P .001; onto wards, median 120 [15320] vs 10 [040], P = .001). packaging with handwritten recycled prescriptions led to more frequent nonspecific aerobic bag surface contamination (63% vs 41%, fisher exact test P = .097), with greater numbers of CFU (median 40 [IQR, 080] VS 0 [040], Mann-Whitney U test, P = .036). The use of printed electronic PN prescriptions can reduce microbial loads for contamination of surfaces that compromises aseptic techniques.

Physicians Failed to Write Flawless Prescriptions When Computerized Physician Order Entry System Crashed.

PubMed

Hsu, Chia-Chen; Chou, Chia-Lin; Chen, Tzeng-Ji; Ho, Chin-Chin; Lee, Chung-Yuan; Chou, Yueh-Ching

2015-05-01

Clinical care has become increasingly dependent on computerized physician order entry (CPOE) systems. No study has reported the adverse effect of CPOE on physicians' ability to handwrite prescriptions. This study took advantage of an extensive crash of the CPOE system at a large hospital to assess the completeness, legibility, and accuracy of physicians' handwritten prescriptions. The CPOE system had operated at the outpatient department of an academic medical center in Taiwan since 1993. During an unintentional shutdown that lasted 3.5 hours in 2010, physicians were forced to write prescriptions manually. These handwritten prescriptions, together with clinical medical records, were later audited by clinical pharmacists with respect to 16 fields of the patient's, prescriber's, and drug data. A total of 1418 prescriptions with 3805 drug items were handwritten by 114 to 1369 patients. Not a single prescription had all necessary fields filled in. Although the field of age was most frequently omitted (1282 [90.4%] of 1418 prescriptions) among the patient's data, the field of dosage form was most frequently omitted (3480 [91.5%] of 3805 items) among the drug data. In contrast, the scale of illegibility was rather small. The highest percentage reached only 1.5% (n = 57) in the field of drug frequency. Inaccuracies of strength, dose, and drug name were observed in 745 (19.6%), 517 (13.6%), and 435 (11.4%) prescribed drug items, respectively. The unintentional shutdown of a long-running CPOE system revealed that physicians fail to handwrite flawless prescriptions in the digital era. The contingency plans for computer disasters at health care facilities might include preparation of stand-alone e-prescribing software so that the service delay could be kept to the minimum. However, guidance on prescribing should remain an essential part of medical education. Copyright © 2015 Elsevier HS Journals, Inc. All rights reserved.

Rotation Reveals the Importance of Configural Cues in Handwritten Word Perception

PubMed Central

Barnhart, Anthony S.; Goldinger, Stephen D.

2013-01-01

A dramatic perceptual asymmetry occurs when handwritten words are rotated 90° in either direction. Those rotated in a direction consistent with their natural tilt (typically clockwise) become much more difficult to recognize, relative to those rotated in the opposite direction. In Experiment 1, we compared computer-printed and handwritten words, all equated for degrees of leftward and rightward tilt, and verified the phenomenon: The effect of rotation was far larger for cursive words, especially when rotated in a tilt-consistent direction. In Experiment 2, we replicated this pattern with all items presented in visual noise. In both experiments, word frequency effects were larger for computer-printed words and did not interact with rotation. The results suggest that handwritten word perception requires greater configural processing, relative to computer print, because handwritten letters are variable and ambiguous. When words are rotated, configural processing suffers, particularly when rotation exaggerates natural tilt. Our account is similar to theories of the “Thatcher Illusion,” wherein face inversion disrupts holistic processing. Together, the findings suggest that configural, word-level processing automatically increases when people read handwriting, as letter-level processing becomes less reliable. PMID:23589201

Text-image alignment for historical handwritten documents

NASA Astrophysics Data System (ADS)

Zinger, S.; Nerbonne, J.; Schomaker, L.

2009-01-01

We describe our work on text-image alignment in context of building a historical document retrieval system. We aim at aligning images of words in handwritten lines with their text transcriptions. The images of handwritten lines are automatically segmented from the scanned pages of historical documents and then manually transcribed. To train automatic routines to detect words in an image of handwritten text, we need a training set - images of words with their transcriptions. We present our results on aligning words from the images of handwritten lines and their corresponding text transcriptions. Alignment based on the longest spaces between portions of handwriting is a baseline. We then show that relative lengths, i.e. proportions of words in their lines, can be used to improve the alignment results considerably. To take into account the relative word length, we define the expressions for the cost function that has to be minimized for aligning text words with their images. We apply right to left alignment as well as alignment based on exhaustive search. The quality assessment of these alignments shows correct results for 69% of words from 100 lines, or 90% of partially correct and correct alignments combined.

Teachers' Perceptions of Digital Badges as Recognition of Professional Development

ERIC Educational Resources Information Center

Jones, W. Monty; Hope, Samantha; Adams, Brianne

2018-01-01

This mixed methods study examined teachers' perceptions and uses of digital badges received as recognition of participation in a professional development program. Quantitative and qualitative survey data was collected from 99 K-12 teachers who were awarded digital badges in Spring 2016. In addition, qualitative data was collected through…

Image Segmentation of Historical Handwriting from Palm Leaf Manuscripts

NASA Astrophysics Data System (ADS)

Surinta, Olarik; Chamchong, Rapeeporn

Palm leaf manuscripts were one of the earliest forms of written media and were used in Southeast Asia to store early written knowledge about subjects such as medicine, Buddhist doctrine and astrology. Therefore, historical handwritten palm leaf manuscripts are important for people who like to learn about historical documents, because we can learn more experience from them. This paper presents an image segmentation of historical handwriting from palm leaf manuscripts. The process is composed of three steps: 1) background elimination to separate text and background by Otsu's algorithm 2) line segmentation and 3) character segmentation by histogram of image. The end result is the character's image. The results from this research may be applied to optical character recognition (OCR) in the future.

Discriminative Features Mining for Offline Handwritten Signature Verification

NASA Astrophysics Data System (ADS)

Neamah, Karrar; Mohamad, Dzulkifli; Saba, Tanzila; Rehman, Amjad

2014-03-01

Signature verification is an active research area in the field of pattern recognition. It is employed to identify the particular person with the help of his/her signature's characteristics such as pen pressure, loops shape, speed of writing and up down motion of pen, writing speed, pen pressure, shape of loops, etc. in order to identify that person. However, in the entire process, features extraction and selection stage is of prime importance. Since several signatures have similar strokes, characteristics and sizes. Accordingly, this paper presents combination of orientation of the skeleton and gravity centre point to extract accurate pattern features of signature data in offline signature verification system. Promising results have proved the success of the integration of the two methods.

Experimental Realization of a Quantum Support Vector Machine

NASA Astrophysics Data System (ADS)

Li, Zhaokai; Liu, Xiaomei; Xu, Nanyang; Du, Jiangfeng

2015-04-01

The fundamental principle of artificial intelligence is the ability of machines to learn from previous experience and do future work accordingly. In the age of big data, classical learning machines often require huge computational resources in many practical cases. Quantum machine learning algorithms, on the other hand, could be exponentially faster than their classical counterparts by utilizing quantum parallelism. Here, we demonstrate a quantum machine learning algorithm to implement handwriting recognition on a four-qubit NMR test bench. The quantum machine learns standard character fonts and then recognizes handwritten characters from a set with two candidates. Because of the wide spread importance of artificial intelligence and its tremendous consumption of computational resources, quantum speedup would be extremely attractive against the challenges of big data.

Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system

NASA Astrophysics Data System (ADS)

Kim, Hyungjin; Hwang, Sungmin; Park, Jungjin; Park, Byung-Gook

2017-10-01

Brain-inspired neuromorphic systems have attracted much attention as new computing paradigms for power-efficient computation. Here, we report a silicon synaptic transistor with two electrically independent gates to realize a hardware-based neural network system without any switching components. The spike-timing dependent plasticity characteristics of the synaptic devices are measured and analyzed. With the help of the device model based on the measured data, the pattern recognition capability of the hardware-based spiking neural network systems is demonstrated using the modified national institute of standards and technology handwritten dataset. By comparing systems with and without inhibitory synapse part, it is confirmed that the inhibitory synapse part is an essential element in obtaining effective and high pattern classification capability.

Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system.

PubMed

Kim, Hyungjin; Hwang, Sungmin; Park, Jungjin; Park, Byung-Gook

2017-10-06

Brain-inspired neuromorphic systems have attracted much attention as new computing paradigms for power-efficient computation. Here, we report a silicon synaptic transistor with two electrically independent gates to realize a hardware-based neural network system without any switching components. The spike-timing dependent plasticity characteristics of the synaptic devices are measured and analyzed. With the help of the device model based on the measured data, the pattern recognition capability of the hardware-based spiking neural network systems is demonstrated using the modified national institute of standards and technology handwritten dataset. By comparing systems with and without inhibitory synapse part, it is confirmed that the inhibitory synapse part is an essential element in obtaining effective and high pattern classification capability.

A bimodal biometric identification system

NASA Astrophysics Data System (ADS)

Laghari, Mohammad S.; Khuwaja, Gulzar A.

2013-03-01

Biometrics consists of methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits. Physicals are related to the shape of the body. Behavioral are related to the behavior of a person. However, biometric authentication systems suffer from imprecision and difficulty in person recognition due to a number of reasons and no single biometrics is expected to effectively satisfy the requirements of all verification and/or identification applications. Bimodal biometric systems are expected to be more reliable due to the presence of two pieces of evidence and also be able to meet the severe performance requirements imposed by various applications. This paper presents a neural network based bimodal biometric identification system by using human face and handwritten signature features.

Overcoming catastrophic forgetting in neural networks

PubMed Central

Kirkpatrick, James; Pascanu, Razvan; Rabinowitz, Neil; Veness, Joel; Desjardins, Guillaume; Rusu, Andrei A.; Milan, Kieran; Quan, John; Ramalho, Tiago; Grabska-Barwinska, Agnieszka; Hassabis, Demis; Clopath, Claudia; Kumaran, Dharshan; Hadsell, Raia

2017-01-01

The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially. PMID:28292907

Interactive-predictive detection of handwritten text blocks

NASA Astrophysics Data System (ADS)

Ramos Terrades, O.; Serrano, N.; Gordó, A.; Valveny, E.; Juan, A.

2010-01-01

A method for text block detection is introduced for old handwritten documents. The proposed method takes advantage of sequential book structure, taking into account layout information from pages previously transcribed. This glance at the past is used to predict the position of text blocks in the current page with the help of conventional layout analysis methods. The method is integrated into the GIDOC prototype: a first attempt to provide integrated support for interactive-predictive page layout analysis, text line detection and handwritten text transcription. Results are given in a transcription task on a 764-page Spanish manuscript from 1891.

Handwritten document age classification based on handwriting styles

NASA Astrophysics Data System (ADS)

Ramaiah, Chetan; Kumar, Gaurav; Govindaraju, Venu

2012-01-01

Handwriting styles are constantly changing over time. We approach the novel problem of estimating the approximate age of Historical Handwritten Documents using Handwriting styles. This system will have many applications in handwritten document processing engines where specialized processing techniques can be applied based on the estimated age of the document. We propose to learn a distribution over styles across centuries using Topic Models and to apply a classifier over weights learned in order to estimate the approximate age of the documents. We present a comparison of different distance metrics such as Euclidean Distance and Hellinger Distance within this application.

21 CFR 11.1 - Scope.

Code of Federal Regulations, 2010 CFR

2010-04-01

... SIGNATURES General Provisions § 11.1 Scope. (a) The regulations in this part set forth the criteria under which the agency considers electronic records, electronic signatures, and handwritten signatures... handwritten signatures executed on paper. (b) This part applies to records in electronic form that are created...

Training the max-margin sequence model with the relaxed slack variables.

PubMed

Niu, Lingfeng; Wu, Jianmin; Shi, Yong

2012-09-01

Sequence models are widely used in many applications such as natural language processing, information extraction and optical character recognition, etc. We propose a new approach to train the max-margin based sequence model by relaxing the slack variables in this paper. With the canonical feature mapping definition, the relaxed problem is solved by training a multiclass Support Vector Machine (SVM). Compared with the state-of-the-art solutions for the sequence learning, the new method has the following advantages: firstly, the sequence training problem is transformed into a multiclassification problem, which is more widely studied and already has quite a few off-the-shelf training packages; secondly, this new approach reduces the complexity of training significantly and achieves comparable prediction performance compared with the existing sequence models; thirdly, when the size of training data is limited, by assigning different slack variables to different microlabel pairs, the new method can use the discriminative information more frugally and produces more reliable model; last but not least, by employing kernels in the intermediate multiclass SVM, nonlinear feature space can be easily explored. Experimental results on the task of named entity recognition, information extraction and handwritten letter recognition with the public datasets illustrate the efficiency and effectiveness of our method. Copyright © 2012 Elsevier Ltd. All rights reserved.

Universal brain systems for recognizing word shapes and handwriting gestures during reading

PubMed Central

Nakamura, Kimihiro; Kuo, Wen-Jui; Pegado, Felipe; Cohen, Laurent; Tzeng, Ovid J. L.; Dehaene, Stanislas

2012-01-01

Do the neural circuits for reading vary across culture? Reading of visually complex writing systems such as Chinese has been proposed to rely on areas outside the classical left-hemisphere network for alphabetic reading. Here, however, we show that, once potential confounds in cross-cultural comparisons are controlled for by presenting handwritten stimuli to both Chinese and French readers, the underlying network for visual word recognition may be more universal than previously suspected. Using functional magnetic resonance imaging in a semantic task with words written in cursive font, we demonstrate that two universal circuits, a shape recognition system (reading by eye) and a gesture recognition system (reading by hand), are similarly activated and show identical patterns of activation and repetition priming in the two language groups. These activations cover most of the brain regions previously associated with culture-specific tuning. Our results point to an extended reading network that invariably comprises the occipitotemporal visual word-form system, which is sensitive to well-formed static letter strings, and a distinct left premotor region, Exner’s area, which is sensitive to the forward or backward direction with which cursive letters are dynamically presented. These findings suggest that cultural effects in reading merely modulate a fixed set of invariant macroscopic brain circuits, depending on surface features of orthographies. PMID:23184998

38 CFR 1.559 - Appeals.

Code of Federal Regulations, 2014 CFR

2014-07-01

... contain an image of the requester's handwritten signature, such as an attachment that shows the requester... confidentiality statute, the email transmission must contain an image of the requester's handwritten signature... processing, e-mail FOIA appeals must be sent to official VA FOIA mailboxes established for the purpose of...

38 CFR 1.559 - Appeals.

Code of Federal Regulations, 2013 CFR

2013-07-01

... contain an image of the requester's handwritten signature, such as an attachment that shows the requester... confidentiality statute, the email transmission must contain an image of the requester's handwritten signature... processing, e-mail FOIA appeals must be sent to official VA FOIA mailboxes established for the purpose of...

38 CFR 1.559 - Appeals.

Code of Federal Regulations, 2012 CFR

2012-07-01

... contain an image of the requester's handwritten signature, such as an attachment that shows the requester... confidentiality statute, the email transmission must contain an image of the requester's handwritten signature... processing, e-mail FOIA appeals must be sent to official VA FOIA mailboxes established for the purpose of...

Simulation Detection in Handwritten Documents by Forensic Document Examiners.

PubMed

Kam, Moshe; Abichandani, Pramod; Hewett, Tom

2015-07-01

This study documents the results of a controlled experiment designed to quantify the abilities of forensic document examiners (FDEs) and laypersons to detect simulations in handwritten documents. Nineteen professional FDEs and 26 laypersons (typical of a jury pool) were asked to inspect test packages that contained six (6) known handwritten documents written by the same person and two (2) questioned handwritten documents. Each questioned document was either written by the person who wrote the known documents, or written by a different person who tried to simulate the writing of the person who wrote the known document. The error rates of the FDEs were smaller than those of the laypersons when detecting simulations in the questioned documents. Among other findings, the FDEs never labeled a questioned document that was written by the same person who wrote the known documents as "simulation." There was a significant statistical difference between the responses of the FDEs and layperson for documents without simulations. © 2015 American Academy of Forensic Sciences.

Character recognition using a neural network model with fuzzy representation

NASA Technical Reports Server (NTRS)

Tavakoli, Nassrin; Seniw, David

1992-01-01

The degree to which digital images are recognized correctly by computerized algorithms is highly dependent upon the representation and the classification processes. Fuzzy techniques play an important role in both processes. In this paper, the role of fuzzy representation and classification on the recognition of digital characters is investigated. An experimental Neural Network model with application to character recognition was developed. Through a set of experiments, the effect of fuzzy representation on the recognition accuracy of this model is presented.

Grading Multiple Choice Exams with Low-Cost and Portable Computer-Vision Techniques

NASA Astrophysics Data System (ADS)

Fisteus, Jesus Arias; Pardo, Abelardo; García, Norberto Fernández

2013-08-01

Although technology for automatic grading of multiple choice exams has existed for several decades, it is not yet as widely available or affordable as it should be. The main reasons preventing this adoption are the cost and the complexity of the setup procedures. In this paper, Eyegrade, a system for automatic grading of multiple choice exams is presented. While most current solutions are based on expensive scanners, Eyegrade offers a truly low-cost solution requiring only a regular off-the-shelf webcam. Additionally, Eyegrade performs both mark recognition as well as optical character recognition of handwritten student identification numbers, which avoids the use of bubbles in the answer sheet. When compared with similar webcam-based systems, the user interface in Eyegrade has been designed to provide a more efficient and error-free data collection procedure. The tool has been validated with a set of experiments that show the ease of use (both setup and operation), the reduction in grading time, and an increase in the reliability of the results when compared with conventional, more expensive systems.

X-ray computed tomography applied to investigate ancient manuscripts

NASA Astrophysics Data System (ADS)

Bettuzzi, Matteo; Albertin, Fauzia; Brancaccio, Rosa; Casali, Franco; Pia Morigi, Maria; Peccenini, Eva

2017-03-01

I will describe in this paper the first results of a series of X-ray tomography applications, with different system setups, running on some ancient manuscripts containing iron-gall ink. The purpose is to verify the optimum measurement conditions with a laboratory instrumentation -that is also in fact portable- in order to recognize the text from the inside of the documents, without opening them. This becomes possible by exploiting the X-rays absorption contrast of iron-based ink and the three-dimensional reconstruction potential provided by computed tomography that overcomes problems that appear in simple radiograph practice. This work is part of a larger project of EPFL (Ecole Polytechnique Fédérale de Lausanne, Switzerland), the "Venice Time Machine" project (EPEL, Digital Heritage Venice, http://dhvenice.eu/, 2015) aimed at digitizing, transcribing and sharing in an open database all the information of the State Archives of Venice, exploiting traditional digitization technologies and innovative methods of acquisition. In this first measurement campaign I investigated a manuscript of the seventeenth century made of a folded sheet; a couple of unopened ancient wills kept in the State Archives in Venice and a handwritten book of several hundred pages of notes of Physics of the nineteenth century.

Automatic forensic face recognition from digital images.

PubMed

Peacock, C; Goode, A; Brett, A

2004-01-01

Digital image evidence is now widely available from criminal investigations and surveillance operations, often captured by security and surveillance CCTV. This has resulted in a growing demand from law enforcement agencies for automatic person-recognition based on image data. In forensic science, a fundamental requirement for such automatic face recognition is to evaluate the weight that can justifiably be attached to this recognition evidence in a scientific framework. This paper describes a pilot study carried out by the Forensic Science Service (UK) which explores the use of digital facial images in forensic investigation. For the purpose of the experiment a specific software package was chosen (Image Metrics Optasia). The paper does not describe the techniques used by the software to reach its decision of probabilistic matches to facial images, but accepts the output of the software as though it were a 'black box'. In this way, the paper lays a foundation for how face recognition systems can be compared in a forensic framework. The aim of the paper is to explore how reliably and under what conditions digital facial images can be presented in evidence.

37 CFR 1.4 - Nature of correspondence and signature requirements.

Code of Federal Regulations, 2014 CFR

2014-07-01

..., that is, have an original handwritten signature personally signed, in permanent dark ink or its... EFS-Web customization. (e) The following correspondence must be submitted with an original handwritten signature personally signed in permanent dark ink or its equivalent: (1) Correspondence requiring a person's...

Network-based high level data classification.

PubMed

Silva, Thiago Christiano; Zhao, Liang

2012-06-01

Traditional supervised data classification considers only physical features (e.g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

Implementation of age and gender recognition system for intelligent digital signage

NASA Astrophysics Data System (ADS)

Lee, Sang-Heon; Sohn, Myoung-Kyu; Kim, Hyunduk

2015-12-01

Intelligent digital signage systems transmit customized advertising and information by analyzing users and customers, unlike existing system that presented advertising in the form of broadcast without regard to type of customers. Currently, development of intelligent digital signage system has been pushed forward vigorously. In this study, we designed a system capable of analyzing gender and age of customers based on image obtained from camera, although there are many different methods for analyzing customers. We conducted age and gender recognition experiments using public database. The age/gender recognition experiments were performed through histogram matching method by extracting Local binary patterns (LBP) features after facial area on input image was normalized. The results of experiment showed that gender recognition rate was as high as approximately 97% on average. Age recognition was conducted based on categorization into 5 age classes. Age recognition rates for women and men were about 67% and 68%, respectively when that conducted separately for different gender.

Jersey number detection in sports video for athlete identification

NASA Astrophysics Data System (ADS)

Ye, Qixiang; Huang, Qingming; Jiang, Shuqiang; Liu, Yang; Gao, Wen

2005-07-01

Athlete identification is important for sport video content analysis since users often care about the video clips with their preferred athletes. In this paper, we propose a method for athlete identification by combing the segmentation, tracking and recognition procedures into a coarse-to-fine scheme for jersey number (digital characters on sport shirt) detection. Firstly, image segmentation is employed to separate the jersey number regions with its background. And size/pipe-like attributes of digital characters are used to filter out candidates. Then, a K-NN (K nearest neighbor) classifier is employed to classify a candidate into a digit in "0-9" or negative. In the recognition procedure, we use the Zernike moment features, which are invariant to rotation and scale for digital shape recognition. Synthetic training samples with different fonts are used to represent the pattern of digital characters with non-rigid deformation. Once a character candidate is detected, a SSD (smallest square distance)-based tracking procedure is started. The recognition procedure is performed every several frames in the tracking process. After tracking tens of frames, the overall recognition results are combined to determine if a candidate is a true jersey number or not by a voting procedure. Experiments on several types of sports video shows encouraging result.

Facial Recognition in a Group-Living Cichlid Fish.

PubMed

Kohda, Masanori; Jordan, Lyndon Alexander; Hotta, Takashi; Kosaka, Naoya; Karino, Kenji; Tanaka, Hirokazu; Taniyama, Masami; Takeyama, Tomohiro

2015-01-01

The theoretical underpinnings of the mechanisms of sociality, e.g. territoriality, hierarchy, and reciprocity, are based on assumptions of individual recognition. While behavioural evidence suggests individual recognition is widespread, the cues that animals use to recognise individuals are established in only a handful of systems. Here, we use digital models to demonstrate that facial features are the visual cue used for individual recognition in the social fish Neolamprologus pulcher. Focal fish were exposed to digital images showing four different combinations of familiar and unfamiliar face and body colorations. Focal fish attended to digital models with unfamiliar faces longer and from a further distance to the model than to models with familiar faces. These results strongly suggest that fish can distinguish individuals accurately using facial colour patterns. Our observations also suggest that fish are able to rapidly (≤ 0.5 sec) discriminate between familiar and unfamiliar individuals, a speed of recognition comparable to primates including humans.

Target and (Astro-)WISE technologies Data federations and its applications

NASA Astrophysics Data System (ADS)

Valentijn, E. A.; Begeman, K.; Belikov, A.; Boxhoorn, D. R.; Brinchmann, J.; McFarland, J.; Holties, H.; Kuijken, K. H.; Verdoes Kleijn, G.; Vriend, W.-J.; Williams, O. R.; Roerdink, J. B. T. M.; Schomaker, L. R. B.; Swertz, M. A.; Tsyganov, A.; van Dijk, G. J. W.

2017-06-01

After its first implementation in 2003 the Astro-WISE technology has been rolled out in several European countries and is used for the production of the KiDS survey data. In the multi-disciplinary Target initiative this technology, nicknamed WISE technology, has been further applied to a large number of projects. Here, we highlight the data handling of other astronomical applications, such as VLT-MUSE and LOFAR, together with some non-astronomical applications such as the medical projects Lifelines and GLIMPS; the MONK handwritten text recognition system; and business applications, by amongst others, the Target Holding. We describe some of the most important lessons learned and describe the application of the data-centric WISE type of approach to the Science Ground Segment of the Euclid satellite.

Approximate message passing with restricted Boltzmann machine priors

NASA Astrophysics Data System (ADS)

Tramel, Eric W.; Drémeau, Angélique; Krzakala, Florent

2016-07-01

Approximate message passing (AMP) has been shown to be an excellent statistical approach to signal inference and compressed sensing problems. The AMP framework provides modularity in the choice of signal prior; here we propose a hierarchical form of the Gauss-Bernoulli prior which utilizes a restricted Boltzmann machine (RBM) trained on the signal support to push reconstruction performance beyond that of simple i.i.d. priors for signals whose support can be well represented by a trained binary RBM. We present and analyze two methods of RBM factorization and demonstrate how these affect signal reconstruction performance within our proposed algorithm. Finally, using the MNIST handwritten digit dataset, we show experimentally that using an RBM allows AMP to approach oracle-support performance.

Use of the DPP4BIT System for the Management of Hospital Medical Equipment.

PubMed

Tsoromokos, Dimitrios; Tsaloukidis, Nikolaos; Zarakovitis, Dimitrios; Lazakidou, Athina

2017-01-01

The Information and Communication Technologies (ICT) combined with the development of innovative skills within the broader health sector, can significantly improve and upgrade health care quality services. The proposed DDP4BIT system supports an alternative channel for digital information recording and equipment handling of Biomedical Technology Departments (BITs) of Health Care Units. This technology is ideal for all types of procedures based on handwritten forms that are commonly used in Health Care Units. The collection of useful statistics for analyzing and exporting data indicators is used in order to reduce ratios, such as operating time ratio, ideal operating time indicator, number of repetitive quality failures, total maintenance cost, etc. and supports decision-making.

Speech Recognition for A Digital Video Library.

ERIC Educational Resources Information Center

Witbrock, Michael J.; Hauptmann, Alexander G.

1998-01-01

Production of the meta-data supporting the Informedia Digital Video Library interface is automated using techniques derived from artificial intelligence research. Speech recognition and natural-language processing, information retrieval, and image analysis are applied to produce an interface that helps users locate information and navigate more…

Digital and optical shape representation and pattern recognition; Proceedings of the Meeting, Orlando, FL, Apr. 4-6, 1988

NASA Technical Reports Server (NTRS)

Juday, Richard D. (Editor)

1988-01-01

The present conference discusses topics in pattern-recognition correlator architectures, digital stereo systems, geometric image transformations and their applications, topics in pattern recognition, filter algorithms, object detection and classification, shape representation techniques, and model-based object recognition methods. Attention is given to edge-enhancement preprocessing using liquid crystal TVs, massively-parallel optical data base management, three-dimensional sensing with polar exponential sensor arrays, the optical processing of imaging spectrometer data, hybrid associative memories and metric data models, the representation of shape primitives in neural networks, and the Monte Carlo estimation of moment invariants for pattern recognition.

Comparison of speech recognition with adaptive digital and FM remote microphone hearing assistance technology by listeners who use hearing aids.

PubMed

Thibodeau, Linda

2014-06-01

The purpose of this study was to compare the benefits of 3 types of remote microphone hearing assistance technology (HAT), adaptive digital broadband, adaptive frequency modulation (FM), and fixed FM, through objective and subjective measures of speech recognition in clinical and real-world settings. Participants included 11 adults, ages 16 to 78 years, with primarily moderate-to-severe bilateral hearing impairment (HI), who wore binaural behind-the-ear hearing aids; and 15 adults, ages 18 to 30 years, with normal hearing. Sentence recognition in quiet and in noise and subjective ratings were obtained in 3 conditions of wireless signal processing. Performance by the listeners with HI when using the adaptive digital technology was significantly better than that obtained with the FM technology, with the greatest benefits at the highest noise levels. The majority of listeners also preferred the digital technology when listening in a real-world noisy environment. The wireless technology allowed persons with HI to surpass persons with normal hearing in speech recognition in noise, with the greatest benefit occurring with adaptive digital technology. The use of adaptive digital technology combined with speechreading cues would allow persons with HI to engage in communication in environments that would have otherwise not been possible with traditional wireless technology.

Thermal-Polarimetric and Visible Data Collection for Face Recognition

DTIC Science & Technology

2016-09-01

pixels • Spectral range: 7.5–13 μm • Analog image output: NTSC analog video • Digital image output: Firewire radiometric, 14-bit digital video to...PC The analog video was not used for this study. The radiometric, 14-bit digital data provided temperature measurement information for comparison...distribution unlimited. 18 9. References 1. Choi J, Hu S, Young SS, Davis LS. Thermal to visible face recognition. Proc. SPIE 8371, Sensing

[Penicher: an manuscript addendum to his pharmacopoeia of 1695 on the copy of Pharmacy College Library].

PubMed

Bonnemain, Bruno

2016-03-01

Penicher's pharmacopeia (1695) was part of the Library of the "College de Pharmacie". The inventory of this Library was done in 1780 and is kept by the Library of the BIU Santé, Paris-Descartes University in Paris that digitized it recently. This copy contains handwritten texts that complete the original edition. The first main addition, at the beginning of the document, is three recipes of drugs, in Latin, one of them being well known at the early 18th century, the vulnerary balm of Leonardo Fioraventi (1517-1588), that is also known as Fioraventi's alcoholate. This product will still be present in the French Codex until 1949. The Penicher' book also includes, at the end, three handwritten pages in French which represent the equipment of apothecaries. These drawings are very close to the ones of Charas' Pharmacopeia. One can think that these additions are from the second part of the 18th century, but before the gift of the pharmacopeia to the College de Pharmacie by Fourcy en 1765. The author is unknown but he is probably one of the predecessor of Fourcy in Pharmacie de l'Ours (Bear's pharmacy). This gift done by Fourcy when joining the Community of Parisians pharmacists did not prevent the fact that Fourcy was sentenced by his colleagues pharmacists, a few years later, for the sales of "Chinese specialties" that someone called Jean-Daniel Smith, a physician installed in Paris, asked him to prepare.

Reading handprinted addresses on IRS tax forms

NASA Astrophysics Data System (ADS)

Ramanaprasad, Vemulapati; Shin, Yong-Chul; Srihari, Sargur N.

1996-03-01

The hand-printed address recognition system described in this paper is a part of the Name and Address Block Reader (NABR) system developed by the Center of Excellence for Document Analysis and Recognition (CEDAR). NABR is currently being used by the IRS to read address blocks (hand-print as well as machine-print) on fifteen different tax forms. Although machine- print address reading was relatively straightforward, hand-print address recognition has posed some special challenges due to demands on processing speed (with an expected throughput of 8450 forms/hour) and recognition accuracy. We discuss various subsystems involved in hand- printed address recognition, including word segmentation, word recognition, digit segmentation, and digit recognition. We also describe control strategies used to make effective use of these subsystems to maximize recognition accuracy. We present system performance on 931 address blocks in recognizing various fields, such as city, state, ZIP Code, street number and name, and personal names.

Comparing Postsecondary Marketing Student Performance on Computer-Based and Handwritten Essay Tests

ERIC Educational Resources Information Center

Truell, Allen D.; Alexander, Melody W.; Davis, Rodney E.

2004-01-01

The purpose of this study was to determine if there were differences in postsecondary marketing student performance on essay tests based on test format (i.e., computer-based or handwritten). Specifically, the variables of performance, test completion time, and gender were explored for differences based on essay test format. Results of the study…

Handwritten Newspapers on the Iowa Frontier, 1844-54.

ERIC Educational Resources Information Center

Atwood, Roy Alden

Journalism on the agricultural frontier of the Old Northwest territory of the United States was shaped by a variety of cultural forces and environmental factors and took on diverse forms. Bridging the gap between the two cultural forms of written correspondence and printed news was a third form: the handwritten newspaper. Between 1844 and 1854…

Judging the Emergent Reading Abilities of Kindergarten Children.

ERIC Educational Resources Information Center

Otto, Beverly; Sulzby, Elizabeth

In 1981, a scale, the Emergent Reading Ability Judgments for Dictated and Handwritten Stories, was developed for use in assessing how close a child was to reading independently based upon the nature of the child's attempts to read from dictated and handwritten stories. A study was conducted to apply the scale to stories from a new sample of…

Spatial Analysis of Handwritten Texts as a Marker of Cognitive Control.

PubMed

Crespo, Y; Soriano, M F; Iglesias-Parro, S; Aznarte, J I; Ibáñez-Molina, A J

2017-12-01

We explore the idea that cognitive demands of the handwriting would influence the degree of automaticity of the handwriting process, which in turn would affect the geometric parameters of texts. We compared the heterogeneity of handwritten texts in tasks with different cognitive demands; the heterogeneity of texts was analyzed with lacunarity, a measure of geometrical invariance. In Experiment 1, we asked participants to perform two tasks that varied in cognitive demands: transcription and exposition about an autobiographical episode. Lacunarity was significantly lower in transcription. In Experiment 2, we compared a veridical and a fictitious version of a personal event. Lacunarity was lower in veridical texts. We contend that differences in lacunarity of handwritten texts reveal the degree of automaticity in handwriting.

Text-line extraction in handwritten Chinese documents based on an energy minimization framework.

PubMed

Koo, Hyung Il; Cho, Nam Ik

2012-03-01

Text-line extraction in unconstrained handwritten documents remains a challenging problem due to nonuniform character scale, spatially varying text orientation, and the interference between text lines. In order to address these problems, we propose a new cost function that considers the interactions between text lines and the curvilinearity of each text line. Precisely, we achieve this goal by introducing normalized measures for them, which are based on an estimated line spacing. We also present an optimization method that exploits the properties of our cost function. Experimental results on a database consisting of 853 handwritten Chinese document images have shown that our method achieves a detection rate of 99.52% and an error rate of 0.32%, which outperforms conventional methods.

Automatic violence detection in digital movies

NASA Astrophysics Data System (ADS)

Fischer, Stephan

1996-11-01

Research on computer-based recognition of violence is scant. We are working on the automatic recognition of violence in digital movies, a first step towards the goal of a computer- assisted system capable of protecting children against TV programs containing a great deal of violence. In the video domain a collision detection and a model-mapping to locate human figures are run, while the creation and comparison of fingerprints to find certain events are run int he audio domain. This article centers on the recognition of fist- fights in the video domain and on the recognition of shots, explosions and cries in the audio domain.

Advanced optical correlation and digital methods for pattern matching—50th anniversary of Vander Lugt matched filter

NASA Astrophysics Data System (ADS)

Millán, María S.

2012-10-01

On the verge of the 50th anniversary of Vander Lugt’s formulation for pattern matching based on matched filtering and optical correlation, we acknowledge the very intense research activity developed in the field of correlation-based pattern recognition during this period of time. The paper reviews some domains that appeared as emerging fields in the last years of the 20th century and have been developed later on in the 21st century. Such is the case of three-dimensional (3D) object recognition, biometric pattern matching, optical security and hybrid optical-digital processors. 3D object recognition is a challenging case of multidimensional image recognition because of its implications in the recognition of real-world objects independent of their perspective. Biometric recognition is essentially pattern recognition for which the personal identification is based on the authentication of a specific physiological characteristic possessed by the subject (e.g. fingerprint, face, iris, retina, and multifactor combinations). Biometric recognition often appears combined with encryption-decryption processes to secure information. The optical implementations of correlation-based pattern recognition processes still rely on the 4f-correlator, the joint transform correlator, or some of their variants. But the many applications developed in the field have been pushing the systems for a continuous improvement of their architectures and algorithms, thus leading towards merged optical-digital solutions.

SU-F-T-20: Novel Catheter Lumen Recognition Algorithm for Rapid Digitization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dise, J; McDonald, D; Ashenafi, M

Purpose: Manual catheter recognition remains a time-consuming aspect of high-dose-rate brachytherapy (HDR) treatment planning. In this work, a novel catheter lumen recognition algorithm was created for accurate and rapid digitization. Methods: MatLab v8.5 was used to create the catheter recognition algorithm. Initially, the algorithm searches the patient CT dataset using an intensity based k-means filter designed to locate catheters. Once the catheters have been located, seed points are manually selected to initialize digitization of each catheter. From each seed point, the algorithm searches locally in order to automatically digitize the remaining catheter. This digitization is accomplished by finding pixels withmore » similar image curvature and divergence parameters compared to the seed pixel. Newly digitized pixels are treated as new seed positions, and hessian image analysis is used to direct the algorithm toward neighboring catheter pixels, and to make the algorithm insensitive to adjacent catheters that are unresolvable on CT, air pockets, and high Z artifacts. The algorithm was tested using 11 HDR treatment plans, including the Syed template, tandem and ovoid applicator, and multi-catheter lung brachytherapy. Digitization error was calculated by comparing manually determined catheter positions to those determined by the algorithm. Results: he digitization error was 0.23 mm ± 0.14 mm axially and 0.62 mm ± 0.13 mm longitudinally at the tip. The time of digitization, following initial seed placement was less than 1 second per catheter. The maximum total time required to digitize all tested applicators was 4 minutes (Syed template with 15 needles). Conclusion: This algorithm successfully digitizes HDR catheters for a variety of applicators with or without CT markers. The minimal axial error demonstrates the accuracy of the algorithm, and its insensitivity to image artifacts and challenging catheter positioning. Future work to automatically place initial seed positions would improve the algorithm speed.« less

Development and validation of a smartphone-based digits-in-noise hearing test in South African English.

PubMed

Potgieter, Jenni-Marí; Swanepoel, De Wet; Myburgh, Hermanus Carel; Hopper, Thomas Christopher; Smits, Cas

2015-07-01

The objective of this study was to develop and validate a smartphone-based digits-in-noise hearing test for South African English. Single digits (0-9) were recorded and spoken by a first language English female speaker. Level corrections were applied to create a set of homogeneous digits with steep speech recognition functions. A smartphone application was created to utilize 120 digit-triplets in noise as test material. An adaptive test procedure determined the speech reception threshold (SRT). Experiments were performed to determine headphones effects on the SRT and to establish normative data. Participants consisted of 40 normal-hearing subjects with thresholds ≤15 dB across the frequency spectrum (250-8000 Hz) and 186 subjects with normal-hearing in both ears, or normal-hearing in the better ear. The results show steep speech recognition functions with a slope of 20%/dB for digit-triplets presented in noise using the smartphone application. The results of five headphone types indicate that the smartphone-based hearing test is reliable and can be conducted using standard Android smartphone headphones or clinical headphones. A digits-in-noise hearing test was developed and validated for South Africa. The mean SRT and speech recognition functions correspond to previous developed telephone-based digits-in-noise tests.

Novel Blind Recognition Algorithm of Frame Synchronization Words Based on Soft-Decision in Digital Communication Systems.

PubMed

Qin, Jiangyi; Huang, Zhiping; Liu, Chunwu; Su, Shaojing; Zhou, Jing

2015-01-01

A novel blind recognition algorithm of frame synchronization words is proposed to recognize the frame synchronization words parameters in digital communication systems. In this paper, a blind recognition method of frame synchronization words based on the hard-decision is deduced in detail. And the standards of parameter recognition are given. Comparing with the blind recognition based on the hard-decision, utilizing the soft-decision can improve the accuracy of blind recognition. Therefore, combining with the characteristics of Quadrature Phase Shift Keying (QPSK) signal, an improved blind recognition algorithm based on the soft-decision is proposed. Meanwhile, the improved algorithm can be extended to other signal modulation forms. Then, the complete blind recognition steps of the hard-decision algorithm and the soft-decision algorithm are given in detail. Finally, the simulation results show that both the hard-decision algorithm and the soft-decision algorithm can recognize the parameters of frame synchronization words blindly. What's more, the improved algorithm can enhance the accuracy of blind recognition obviously.

The electronic, 'paperless' medical office; has it arrived?

PubMed

Gates, P; Urquhart, J

2007-02-01

Modern information technology offers efficiencies in medical practice, with a reduction in secretarial time in maintaining, filing and retrieving the paper medical record. Electronic requesting of investigations allows tracking of outstanding results. Less storage space is required and telephone calls from pharmacies, pathology and medical imaging service providers to clarify the hand-written request are abolished. Voice recognition software reduces secretarial typing time per letter. These combined benefits can lead to significantly reduced costs and improved patient care. The paperless office is possible, but requires commitment and training of all staff; it is preferable but not absolutely essential that at least one member of the practice has an interest and some expertise in computers. More importantly, back-up from information technology providers and back-up of the electronic data are absolutely crucial and a paperless environment should not be considered without them.

A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification

PubMed Central

Wen, Cuihong; Zhang, Jing; Rebelo, Ana; Cheng, Fanyong

2016-01-01

Optical Music Recognition (OMR) has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM). The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM), which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs) and Neural Networks (NNs). PMID:26985826

A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification.

PubMed

Wen, Cuihong; Zhang, Jing; Rebelo, Ana; Cheng, Fanyong

2016-01-01

Optical Music Recognition (OMR) has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM). The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM), which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs) and Neural Networks (NNs).

Spin orbit torque based electronic neuron

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sengupta, Abhronil, E-mail: asengup@purdue.edu; Choday, Sri Harsha; Kim, Yusung

2015-04-06

A device based on current-induced spin-orbit torque (SOT) that functions as an electronic neuron is proposed in this work. The SOT device implements an artificial neuron's thresholding (transfer) function. In the first step of a two-step switching scheme, a charge current places the magnetization of a nano-magnet along the hard-axis, i.e., an unstable point for the magnet. In the second step, the SOT device (neuron) receives a current (from the synapses) which moves the magnetization from the unstable point to one of the two stable states. The polarity of the synaptic current encodes the excitatory and inhibitory nature of themore » neuron input and determines the final orientation of the magnetization. A resistive crossbar array, functioning as synapses, generates a bipolar current that is a weighted sum of the inputs. The simulation of a two layer feed-forward artificial neural network based on the SOT electronic neuron shows that it consumes ∼3× lower power than a 45 nm digital CMOS implementation, while reaching ∼80% accuracy in the classification of 100 images of handwritten digits from the MNIST dataset.« less

Task-driven dictionary learning.

PubMed

Mairal, Julien; Bach, Francis; Ponce, Jean

2012-04-01

Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience, and signal processing. For signals such as natural images that admit such sparse representations, it is now well established that these models are well suited to restoration tasks. In this context, learning the dictionary amounts to solving a large-scale matrix factorization problem, which can be done efficiently with classical optimization tools. The same approach has also been used for learning features from data for other purposes, e.g., image classification, but tuning the dictionary in a supervised way for these tasks has proven to be more difficult. In this paper, we present a general formulation for supervised dictionary learning adapted to a wide variety of tasks, and present an efficient algorithm for solving the corresponding optimization problem. Experiments on handwritten digit classification, digital art identification, nonlinear inverse image problems, and compressed sensing demonstrate that our approach is effective in large-scale settings, and is well suited to supervised and semi-supervised classification, as well as regression tasks for data that admit sparse representations.

Supporting Learning with Weblogs in Science Education: A Comparison of Blogging and Hand-Written Reflective Writing with and without Prompts

ERIC Educational Resources Information Center

Petko, Dominik; Egger, Nives; Graber, Marc

2014-01-01

The goal of this study was to compare how weblogs and traditional handwritten reflective learning protocols compare regarding the use of cognitive and metacognitive strategies for knowledge acquisition as well as learning gains in secondary school students. The study used a quasi-experimental control group design with repeated measurements…

A survey of user acceptance of electronic patient anesthesia records

PubMed Central

Jin, Hyun Seung; Lee, Suk Young; Jeong, Hui Yeon; Choi, Soo Joo; Lee, Hye Won

2012-01-01

Background An anesthesia information management system (AIMS), although not widely used in Korea, will eventually replace handwritten records. This hospital began using AIMS in April 2010. The purpose of this study was to evaluate users' attitudes concerning AIMS and to compare them with manual documentation in the operating room (OR). Methods A structured questionnaire focused on satisfaction with electronic anesthetic records and comparison with handwritten anesthesia records was administered to anesthesiologists, trainees, and nurses during February 2011 and the responses were collected anonymously during March 2011. Results A total of 28 anesthesiologists, 27 trainees, and 47 nurses responded to this survey. Most participants involved in this survey were satisfied with AIMS (96.3%, 82.2%, and 89.3% of trainees, anesthesiologists, and nurses, respectively) and preferred AIMS over handwritten anesthesia records in 96.3%, 71.4%, and 97.9% of trainees, anesthesiologists, and nurses, respectively. However, there were also criticisms of AIMS related to user-discomfort during short, simple or emergency surgeries, doubtful legal status, and inconvenient placement of the system. Conclusions Overall, most of the anesthetic practitioners in this hospital quickly accepted and prefer AIMS over the handwritten anesthetic records in the OR. PMID:22558502

Word recognition materials for native speakers of Taiwan Mandarin.

PubMed

Nissen, Shawn L; Harris, Richard W; Dukes, Alycia

2008-06-01

To select, digitally record, evaluate, and psychometrically equate word recognition materials that can be used to measure the speech perception abilities of native speakers of Taiwan Mandarin in quiet. Frequently used bisyllabic words produced by male and female talkers of Taiwan Mandarin were digitally recorded and subsequently evaluated using 20 native listeners with normal hearing at 10 intensity levels (-5 to 40 dB HL) in increments of 5 dB. Using logistic regression, 200 words with the steepest psychometric slopes were divided into 4 lists and 8 half-lists that were relatively equivalent in psychometric function slope. To increase auditory homogeneity of the lists, the intensity of words in each list was digitally adjusted so that the threshold of each list was equal to the midpoint between the mean thresholds of the male and female half-lists. Digital recordings of the word recognition lists and the associated clinical instructions are available on CD upon request.

Dilated contour extraction and component labeling algorithm for object vector representation

NASA Astrophysics Data System (ADS)

Skourikhine, Alexei N.

2005-08-01

Object boundary extraction from binary images is important for many applications, e.g., image vectorization, automatic interpretation of images containing segmentation results, printed and handwritten documents and drawings, maps, and AutoCAD drawings. Efficient and reliable contour extraction is also important for pattern recognition due to its impact on shape-based object characterization and recognition. The presented contour tracing and component labeling algorithm produces dilated (sub-pixel) contours associated with corresponding regions. The algorithm has the following features: (1) it always produces non-intersecting, non-degenerate contours, including the case of one-pixel wide objects; (2) it associates the outer and inner (i.e., around hole) contours with the corresponding regions during the process of contour tracing in a single pass over the image; (3) it maintains desired connectivity of object regions as specified by 8-neighbor or 4-neighbor connectivity of adjacent pixels; (4) it avoids degenerate regions in both background and foreground; (5) it allows an easy augmentation that will provide information about the containment relations among regions; (6) it has a time complexity that is dominantly linear in the number of contour points. This early component labeling (contour-region association) enables subsequent efficient object-based processing of the image information.

Optical and digital pattern recognition; Proceedings of the Meeting, Los Angeles, CA, Jan. 13-15, 1987

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang (Editor); Schenker, Paul (Editor)

1987-01-01

The papers presented in this volume provide an overview of current research in both optical and digital pattern recognition, with a theme of identifying overlapping research problems and methodologies. Topics discussed include image analysis and low-level vision, optical system design, object analysis and recognition, real-time hybrid architectures and algorithms, high-level image understanding, and optical matched filter design. Papers are presented on synthetic estimation filters for a control system; white-light correlator character recognition; optical AI architectures for intelligent sensors; interpreting aerial photographs by segmentation and search; and optical information processing using a new photopolymer.

Mexican sign language recognition using normalized moments and artificial neural networks

NASA Astrophysics Data System (ADS)

Solís-V., J.-Francisco; Toxqui-Quitl, Carina; Martínez-Martínez, David; H.-G., Margarita

2014-09-01

This work presents a framework designed for the Mexican Sign Language (MSL) recognition. A data set was recorded with 24 static signs from the MSL using 5 different versions, this MSL dataset was captured using a digital camera in incoherent light conditions. Digital Image Processing was used to segment hand gestures, a uniform background was selected to avoid using gloved hands or some special markers. Feature extraction was performed by calculating normalized geometric moments of gray scaled signs, then an Artificial Neural Network performs the recognition using a 10-fold cross validation tested in weka, the best result achieved 95.83% of recognition rate.

Decomposition-based transfer distance metric learning for image classification.

PubMed

Luo, Yong; Liu, Tongliang; Tao, Dacheng; Xu, Chao

2014-09-01

Distance metric learning (DML) is a critical factor for image analysis and pattern recognition. To learn a robust distance metric for a target task, we need abundant side information (i.e., the similarity/dissimilarity pairwise constraints over the labeled data), which is usually unavailable in practice due to the high labeling cost. This paper considers the transfer learning setting by exploiting the large quantity of side information from certain related, but different source tasks to help with target metric learning (with only a little side information). The state-of-the-art metric learning algorithms usually fail in this setting because the data distributions of the source task and target task are often quite different. We address this problem by assuming that the target distance metric lies in the space spanned by the eigenvectors of the source metrics (or other randomly generated bases). The target metric is represented as a combination of the base metrics, which are computed using the decomposed components of the source metrics (or simply a set of random bases); we call the proposed method, decomposition-based transfer DML (DTDML). In particular, DTDML learns a sparse combination of the base metrics to construct the target metric by forcing the target metric to be close to an integration of the source metrics. The main advantage of the proposed method compared with existing transfer metric learning approaches is that we directly learn the base metric coefficients instead of the target metric. To this end, far fewer variables need to be learned. We therefore obtain more reliable solutions given the limited side information and the optimization tends to be faster. Experiments on the popular handwritten image (digit, letter) classification and challenge natural image annotation tasks demonstrate the effectiveness of the proposed method.

Preschoolers Explore Interactive Storybook Apps: The Effect on Word Recognition and Story Comprehension

ERIC Educational Resources Information Center

Zipke, Marcy

2017-01-01

Two experiments explored the effects of reading digital storybooks on tablet computers with 25 preschoolers, aged 4-5. In the first experiment, the students' word recognition scores were found to increase significantly more when students explored a digital storybook and employed the read-aloud function than when they were read to from a comparable…

Biometrics Foundation Documents

DTIC Science & Technology

2009-01-01

a digital form. The quality of the sensor used has a significant impact on the recognition results. Example “sensors” could be digital cameras...Difficult to control sensor and channel variances that significantly impact capabilities Not sufficiently distinctive for identification over large...expressions, hairstyle, glasses, hats, makeup, etc. have on face recognition systems? Minor variances , such as those mentioned, will have a moderate

Digitization of Full-Text Documents Before Publishing on the Internet: A Case Study Reviewing the Latest Optical Character Recognition Technologies.

ERIC Educational Resources Information Center

McClean, Clare M.

1998-01-01

Reviews strengths and weaknesses of five optical character recognition (OCR) software packages used to digitize paper documents before publishing on the Internet. Outlines options available and stages of the conversion process. Describes the learning experience of Eurotext, a United Kingdom-based electronic libraries project (eLib). (PEN)

Evaluation of Hand Written and Computerized Out-Patient Prescriptions in Urban Part of Central Gujarat.

PubMed

Joshi, Anuradha; Buch, Jatin; Kothari, Nitin; Shah, Nishal

2016-06-01

Prescription order is an important therapeutic transaction between physician and patient. A good quality prescription is an extremely important factor for minimizing errors in dispensing medication and it should be adherent to guidelines for prescription writing for benefit of the patient. To evaluate frequency and type of prescription errors in outpatient prescriptions and find whether prescription writing abides with WHO standards of prescription writing. A cross-sectional observational study was conducted at Anand city. Allopathic private practitioners practising at Anand city of different specialities were included in study. Collection of prescriptions was started a month after the consent to minimize bias in prescription writing. The prescriptions were collected from local pharmacy stores of Anand city over a period of six months. Prescriptions were analysed for errors in standard information, according to WHO guide to good prescribing. Descriptive analysis was performed to estimate frequency of errors, data were expressed as numbers and percentage. Total 749 (549 handwritten and 200 computerised) prescriptions were collected. Abundant omission errors were identified in handwritten prescriptions e.g., OPD number was mentioned in 6.19%, patient's age was mentioned in 25.50%, gender in 17.30%, address in 9.29% and weight of patient mentioned in 11.29%, while in drug items only 2.97% drugs were prescribed by generic name. Route and Dosage form was mentioned in 77.35%-78.15%, dose mentioned in 47.25%, unit in 13.91%, regimens were mentioned in 72.93% while signa (direction for drug use) in 62.35%. Total 4384 errors out of 549 handwritten prescriptions and 501 errors out of 200 computerized prescriptions were found in clinicians and patient details. While in drug item details, total number of errors identified were 5015 and 621 in handwritten and computerized prescriptions respectively. As compared to handwritten prescriptions, computerized prescriptions appeared to be associated with relatively lower rates of error. Since out-patient prescription errors are abundant and often occur in handwritten prescriptions, prescribers need to adapt themselves to computerized prescription order entry in their daily practice.

Evaluation of Hand Written and Computerized Out-Patient Prescriptions in Urban Part of Central Gujarat

PubMed Central

Buch, Jatin; Kothari, Nitin; Shah, Nishal

2016-01-01

Introduction Prescription order is an important therapeutic transaction between physician and patient. A good quality prescription is an extremely important factor for minimizing errors in dispensing medication and it should be adherent to guidelines for prescription writing for benefit of the patient. Aim To evaluate frequency and type of prescription errors in outpatient prescriptions and find whether prescription writing abides with WHO standards of prescription writing. Materials and Methods A cross-sectional observational study was conducted at Anand city. Allopathic private practitioners practising at Anand city of different specialities were included in study. Collection of prescriptions was started a month after the consent to minimize bias in prescription writing. The prescriptions were collected from local pharmacy stores of Anand city over a period of six months. Prescriptions were analysed for errors in standard information, according to WHO guide to good prescribing. Statistical Analysis Descriptive analysis was performed to estimate frequency of errors, data were expressed as numbers and percentage. Results Total 749 (549 handwritten and 200 computerised) prescriptions were collected. Abundant omission errors were identified in handwritten prescriptions e.g., OPD number was mentioned in 6.19%, patient’s age was mentioned in 25.50%, gender in 17.30%, address in 9.29% and weight of patient mentioned in 11.29%, while in drug items only 2.97% drugs were prescribed by generic name. Route and Dosage form was mentioned in 77.35%-78.15%, dose mentioned in 47.25%, unit in 13.91%, regimens were mentioned in 72.93% while signa (direction for drug use) in 62.35%. Total 4384 errors out of 549 handwritten prescriptions and 501 errors out of 200 computerized prescriptions were found in clinicians and patient details. While in drug item details, total number of errors identified were 5015 and 621 in handwritten and computerized prescriptions respectively. Conclusion As compared to handwritten prescriptions, computerized prescriptions appeared to be associated with relatively lower rates of error. Since out-patient prescription errors are abundant and often occur in handwritten prescriptions, prescribers need to adapt themselves to computerized prescription order entry in their daily practice. PMID:27504305

[About da tai - abortion in old Chinese folk medicine handwritten manuscripts].

PubMed

Zheng, Jinsheng

2013-01-01

Of 881 Chinese handwritten volumes with medical texts of the 17th through mid-20th century held by Staatsbibliothek zu Berlin and Ethnologisches Museum Berlin-Dahlem, 48 volumes include prescriptions for induced abortion. A comparison shows that these records are significantly different from references to abortion in Chinese printed medical texts of pre-modern times. For example, the percentage of recipes recommended for artificial abortions in handwritten texts is significantly higher than those in printed medical books. Authors of handwritten texts used 25 terms to designate artificial abortion, with the term da tai [see text], lit.: "to strike the fetus", occurring most frequently. Its meaning is well defined, in contrast to other terms used, such as duo tai [see text], lit: "to make a fetus fall", xia tai [see text], lit. "to bring a fetus down", und duan chan [see text], lit., to interrupt birthing", which is mostly used to indicate a temporary or permanent sterilization. Pre-modern Chinese medicine has not generally abstained from inducing abortions; physicians showed a differentiating attitude. While abortions were descibed as "things a [physician with an attitude of] humaneness will not do", in case a pregnancy was seen as too risky for a woman she was offered medication to terminate this pregnancy. The commercial application of abortifacients has been recorded in China since ancient times. A request for such services has continued over time for various reasons, including so-called illegitimate pregnancies, and those by nuns, widows and prostitutes. In general, recipes to induce abortions documented in printed medical literature have mild effects and are to be ingested orally. In comparison, those recommended in handwritten texts are rather toxic. Possibly to minimize the negative side-effects of such medication, practitioners of folk medicine developed mechanical devices to perform "external", i.e., vaginal approaches.

A randomized comparison between records made with an anesthesia information management system and by hand, and evaluation of the Hawthorne effect.

PubMed

Edwards, Kylie-Ellen; Hagen, Sander M; Hannam, Jacqueline; Kruger, Cornelis; Yu, Richard; Merry, Alan F

2013-10-01

Anesthesia information management system (AIMS) technology is designed to facilitate high-quality anesthetic recordkeeping. We examined the hypothesis that no difference exists between AIMS and handwritten anesthetic records in regard to the completeness of important information contained as text data. We also investigated the effect of observational research on the completeness of anesthesiologists' recordkeeping. As part of a larger randomized controlled trial, participants were randomized to produce 400 anesthetic records, either handwritten (n = 200) or using an AIMS (n = 200). Records were assessed against a 32-item checklist modified from a clinical guideline. Intravenous agent and bolus recordings were quantified, and data were compared between handwritten and AIMS records. Records produced with intensive research observation during the initial phase of the study (n = 200) were compared with records produced with reduced intensity observation during the final phase of the study (n = 200). The AIMS records were more complete than the handwritten records (mean difference 7.1%; 95% confidence interval [CI] 5.6 to 8.6%; P < 0.0001), with higher completion rates for six individual items on the checklist (P < 0.0001). Drug annotation data were equal between arms. The records completed early in the study, during a period of more intense observation, were more thorough than subsequent records (87.3% vs 81.6%, respectively; mean difference 5.7%; 95% CI 4.2 to 7.3%; P < 0.0001). The AIMS records were more complete than the handwritten records for 32 predefined items. The potential of observational research to influence professional behaviour in an anesthetic context was confirmed. This trial was registered at the Australian New Zealand Clinical Trials Registry No 12608000068369.

Annotation an effective device for student feedback: a critical review of the literature.

PubMed

Ball, Elaine C

2010-05-01

The paper examines hand-written annotation, its many features, difficulties and strengths as a feedback tool. It extends and clarifies what modest evidence is in the public domain and offers an evaluation of how to use annotation effectively in the support of student feedback [Marshall, C.M., 1998a. The Future of Annotation in a Digital (paper) World. Presented at the 35th Annual GLSLIS Clinic: Successes and Failures of Digital Libraries, June 20-24, University of Illinois at Urbana-Champaign, March 24, pp. 1-20; Marshall, C.M., 1998b. Toward an ecology of hypertext annotation. Hypertext. In: Proceedings of the Ninth ACM Conference on Hypertext and Hypermedia, June 20-24, Pittsburgh Pennsylvania, US, pp. 40-49; Wolfe, J.L., Nuewirth, C.M., 2001. From the margins to the centre: the future of annotation. Journal of Business and Technical Communication, 15(3), 333-371; Diyanni, R., 2002. One Hundred Great Essays. Addison-Wesley, New York; Wolfe, J.L., 2002. Marginal pedagogy: how annotated texts affect writing-from-source texts. Written Communication, 19(2), 297-333; Liu, K., 2006. Annotation as an index to critical writing. Urban Education, 41, 192-207; Feito, A., Donahue, P., 2008. Minding the gap annotation as preparation for discussion. Arts and Humanities in Higher Education, 7(3), 295-307; Ball, E., 2009. A participatory action research study on handwritten annotation feedback and its impact on staff and students. Systemic Practice and Action Research, 22(2), 111-124; Ball, E., Franks, H., McGrath, M., Leigh, J., 2009. Annotation is a valuable tool to enhance learning and assessment in student essays. Nurse Education Today, 29(3), 284-291]. Although a significant number of studies examine annotation, this is largely related to on-line tools and computer mediated communication and not hand-written annotation as comment, phrase or sign written on the student essay to provide critique. Little systematic research has been conducted to consider how this latter form of annotation influences student learning and assessment or, indeed, helps tutors to employ better annotative practices [Juwah, C., Macfarlane-Dick, D., Matthew, B., Nicol, D., Ross, D., Smith, B., 2004. Enhancing student learning through effective formative feedback. The Higher Education Academy, 1-40; Jewitt, C., Kress, G., 2005. English in classrooms: only write down what you need to know: annotation for what? English in Education, 39(1), 5-18]. There is little evidence on ways to heighten students' self-awareness when their essays are returned with annotated feedback [Storch, N., Tapper, J., 1997. Student annotations: what NNS and NS university students say about their own writing. Journal of Second Language Writing, 6(3), 245-265]. The literature review clarifies forms of annotation as feedback practice and offers a summary of the challenges and usefulness of annotation. Copyright 2009. Published by Elsevier Ltd.

Marker Registration Technique for Handwritten Text Marker in Augmented Reality Applications

NASA Astrophysics Data System (ADS)

Thanaborvornwiwat, N.; Patanukhom, K.

2018-04-01

Marker registration is a fundamental process to estimate camera poses in marker-based Augmented Reality (AR) systems. We developed AR system that creates correspondence virtual objects on handwritten text markers. This paper presents a new method for registration that is robust for low-content text markers, variation of camera poses, and variation of handwritten styles. The proposed method uses Maximally Stable Extremal Regions (MSER) and polygon simplification for a feature point extraction. The experiment shows that we need to extract only five feature points per image which can provide the best registration results. An exhaustive search is used to find the best matching pattern of the feature points in two images. We also compared performance of the proposed method to some existing registration methods and found that the proposed method can provide better accuracy and time efficiency.

Active Learning with Irrelevant Examples

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri; Mazzoni, Dominic

2009-01-01

An improved active learning method has been devised for training data classifiers. One example of a data classifier is the algorithm used by the United States Postal Service since the 1960s to recognize scans of handwritten digits for processing zip codes. Active learning algorithms enable rapid training with minimal investment of time on the part of human experts to provide training examples consisting of correctly classified (labeled) input data. They function by identifying which examples would be most profitable for a human expert to label. The goal is to maximize classifier accuracy while minimizing the number of examples the expert must label. Although there are several well-established methods for active learning, they may not operate well when irrelevant examples are present in the data set. That is, they may select an item for labeling that the expert simply cannot assign to any of the valid classes. In the context of classifying handwritten digits, the irrelevant items may include stray marks, smudges, and mis-scans. Querying the expert about these items results in wasted time or erroneous labels, if the expert is forced to assign the item to one of the valid classes. In contrast, the new algorithm provides a specific mechanism for avoiding querying the irrelevant items. This algorithm has two components: an active learner (which could be a conventional active learning algorithm) and a relevance classifier. The combination of these components yields a method, denoted Relevance Bias, that enables the active learner to avoid querying irrelevant data so as to increase its learning rate and efficiency when irrelevant items are present. The algorithm collects irrelevant data in a set of rejected examples, then trains the relevance classifier to distinguish between labeled (relevant) training examples and the rejected ones. The active learner combines its ranking of the items with the probability that they are relevant to yield a final decision about which item to present to the expert for labeling. Experiments on several data sets have demonstrated that the Relevance Bias approach significantly decreases the number of irrelevant items queried and also accelerates learning speed.

For Those of Us at the Borders: Recognition and Evaluation of Faculty Work in the Academic Field of Film and Digital Media

ERIC Educational Resources Information Center

Collins, E. Anthony

2011-01-01

Artistic, scholarly, and professional works by individual faculty members in the field of film and digital media are not being adequately recognized or rewarded as scholarship activity during performance evaluation in institutions of higher learning. Conventional systems for the recognition and evaluation of work prioritize scientism and compel…

Use of Adaptive Digital Signal Processing to Improve Speech Communication for Normally Hearing aand Hearing-Impaired Subjects.

ERIC Educational Resources Information Center

Harris, Richard W.; And Others

1988-01-01

A two-microphone adaptive digital noise cancellation technique improved word-recognition ability for 20 normal and 12 hearing-impaired adults by reducing multitalker speech babble and speech spectrum noise 18-22 dB. Word recognition improvements averaged 37-50 percent for normal and 27-40 percent for hearing-impaired subjects. Improvement was best…

Spotting handwritten words and REGEX using a two stage BLSTM-HMM architecture

NASA Astrophysics Data System (ADS)

Bideault, Gautier; Mioulet, Luc; Chatelain, Clément; Paquet, Thierry

2015-01-01

In this article, we propose a hybrid model for spotting words and regular expressions (REGEX) in handwritten documents. The model is made of the state-of-the-art BLSTM (Bidirectional Long Short Time Memory) neural network for recognizing and segmenting characters, coupled with a HMM to build line models able to spot the desired sequences. Experiments on the Rimes database show very promising results.

Spotting words in handwritten Arabic documents

NASA Astrophysics Data System (ADS)

Srihari, Sargur; Srinivasan, Harish; Babu, Pavithra; Bhole, Chetan

2006-01-01

The design and performance of a system for spotting handwritten Arabic words in scanned document images is presented. Three main components of the system are a word segmenter, a shape based matcher for words and a search interface. The user types in a query in English within a search window, the system finds the equivalent Arabic word, e.g., by dictionary look-up, locates word images in an indexed (segmented) set of documents. A two-step approach is employed in performing the search: (1) prototype selection: the query is used to obtain a set of handwritten samples of that word from a known set of writers (these are the prototypes), and (2) word matching: the prototypes are used to spot each occurrence of those words in the indexed document database. A ranking is performed on the entire set of test word images-- where the ranking criterion is a similarity score between each prototype word and the candidate words based on global word shape features. A database of 20,000 word images contained in 100 scanned handwritten Arabic documents written by 10 different writers was used to study retrieval performance. Using five writers for providing prototypes and the other five for testing, using manually segmented documents, 55% precision is obtained at 50% recall. Performance increases as more writers are used for training.

Rapid detection of malignant bio-species using digital holographic pattern recognition and nano-photonics

NASA Astrophysics Data System (ADS)

Sarkisov, Sergey S.; Kukhtareva, Tatiana; Kukhtarev, Nickolai V.; Curley, Michael J.; Edwards, Vernessa; Creer, Marylyn

2013-03-01

There is a great need for rapid detection of bio-hazardous species particularly in applications to food safety and biodefense. It has been recently demonstrated that the colonies of various bio-species could be rapidly detected using culture-specific and reproducible patterns generated by scattered non-coherent light. However, the method heavily relies on a digital pattern recognition algorithm, which is rather complex, requires substantial computational power and is prone to ambiguities due to shift, scale, or orientation mismatch between the analyzed pattern and the reference from the library. The improvement could be made, if, in addition to the intensity of the scattered optical wave, its phase would be also simultaneously recorded and used for the digital holographic pattern recognition. In this feasibility study the research team recorded digital Gabor-type (in-line) holograms of colonies of micro-organisms, such as Salmonella with a laser diode as a low-coherence light source and a lensless high-resolution (2.0x2.0 micron pixel pitch) digital image sensor. The colonies were grown in conventional Petri dishes using standard methods. The digitally recorded holograms were used for computational reconstruction of the amplitude and phase information of the optical wave diffracted on the colonies. Besides, the pattern recognition of the colony fragments using the cross-correlation between the digital hologram was also implemented. The colonies of mold fungi Altenaria sp, Rhizophus, sp, and Aspergillus sp have been also generating nano-colloidal silver during their growth in specially prepared matrices. The silver-specific plasmonic optical extinction peak at 410-nm was also used for rapid detection and growth monitoring of the fungi colonies.

Extrinsic Cognitive Load Impairs Spoken Word Recognition in High- and Low-Predictability Sentences.

PubMed

Hunter, Cynthia R; Pisoni, David B

Listening effort (LE) induced by speech degradation reduces performance on concurrent cognitive tasks. However, a converse effect of extrinsic cognitive load on recognition of spoken words in sentences has not been shown. The aims of the present study were to (a) examine the impact of extrinsic cognitive load on spoken word recognition in a sentence recognition task and (b) determine whether cognitive load and/or LE needed to understand spectrally degraded speech would differentially affect word recognition in high- and low-predictability sentences. Downstream effects of speech degradation and sentence predictability on the cognitive load task were also examined. One hundred twenty young adults identified sentence-final spoken words in high- and low-predictability Speech Perception in Noise sentences. Cognitive load consisted of a preload of short (low-load) or long (high-load) sequences of digits, presented visually before each spoken sentence and reported either before or after identification of the sentence-final word. LE was varied by spectrally degrading sentences with four-, six-, or eight-channel noise vocoding. Level of spectral degradation and order of report (digits first or words first) were between-participants variables. Effects of cognitive load, sentence predictability, and speech degradation on accuracy of sentence-final word identification as well as recall of preload digit sequences were examined. In addition to anticipated main effects of sentence predictability and spectral degradation on word recognition, we found an effect of cognitive load, such that words were identified more accurately under low load than high load. However, load differentially affected word identification in high- and low-predictability sentences depending on the level of sentence degradation. Under severe spectral degradation (four-channel vocoding), the effect of cognitive load on word identification was present for high-predictability sentences but not for low-predictability sentences. Under mild spectral degradation (eight-channel vocoding), the effect of load was present for low-predictability sentences but not for high-predictability sentences. There were also reliable downstream effects of speech degradation and sentence predictability on recall of the preload digit sequences. Long digit sequences were more easily recalled following spoken sentences that were less spectrally degraded. When digits were reported after identification of sentence-final words, short digit sequences were recalled more accurately when the spoken sentences were predictable. Extrinsic cognitive load can impair recognition of spectrally degraded spoken words in a sentence recognition task. Cognitive load affected word identification in both high- and low-predictability sentences, suggesting that load may impact both context use and lower-level perceptual processes. Consistent with prior work, LE also had downstream effects on memory for visual digit sequences. Results support the proposal that extrinsic cognitive load and LE induced by signal degradation both draw on a central, limited pool of cognitive resources that is used to recognize spoken words in sentences under adverse listening conditions.

Vital sign documentation in electronic records: The development of workarounds.

PubMed

Stevenson, Jean E; Israelsson, Johan; Nilsson, Gunilla; Petersson, Goran; Bath, Peter A

2018-06-01

Workarounds are commonplace in healthcare settings. An increase in the use of electronic health records has led to an escalation of workarounds as healthcare professionals cope with systems which are inadequate for their needs. Closely related to this, the documentation of vital signs in electronic health records has been problematic. The accuracy and completeness of vital sign documentation has a direct impact on the recognition of deterioration in a patient's condition. We examined workflow processes to identify workarounds related to vital signs in a 372-bed hospital in Sweden. In three clinical areas, a qualitative study was performed with data collected during observations and interviews and analysed through thematic content analysis. We identified paper workarounds in the form of handwritten notes and a total of eight pre-printed paper observation charts. Our results suggested that nurses created workarounds to allow a smooth workflow and ensure patients safety.

Questioned document workflow for handwriting with automated tools

NASA Astrophysics Data System (ADS)

Das, Krishnanand; Srihari, Sargur N.; Srinivasan, Harish

2012-01-01

During the last few years many document recognition methods have been developed to determine whether a handwriting specimen can be attributed to a known writer. However, in practice, the work-flow of the document examiner continues to be manual-intensive. Before a systematic or computational, approach can be developed, an articulation of the steps involved in handwriting comparison is needed. We describe the work flow of handwritten questioned document examination, as described in a standards manual, and the steps where existing automation tools can be used. A well-known ransom note case is considered as an example, where one encounters testing for multiple writers of the same document, determining whether the writing is disguised, known writing is formal while questioned writing is informal, etc. The findings for the particular ransom note case using the tools are given. Also observations are made for developing a more fully automated approach to handwriting examination.

Machine printed text and handwriting identification in noisy document images.

PubMed

Zheng, Yefeng; Li, Huiping; Doermann, David

2004-03-01

In this paper, we address the problem of the identification of text in noisy document images. We are especially focused on segmenting and identifying between handwriting and machine printed text because: 1) Handwriting in a document often indicates corrections, additions, or other supplemental information that should be treated differently from the main content and 2) the segmentation and recognition techniques requested for machine printed and handwritten text are significantly different. A novel aspect of our approach is that we treat noise as a separate class and model noise based on selected features. Trained Fisher classifiers are used to identify machine printed text and handwriting from noise and we further exploit context to refine the classification. A Markov Random Field-based (MRF) approach is used to model the geometrical structure of the printed text, handwriting, and noise to rectify misclassifications. Experimental results show that our approach is robust and can significantly improve page segmentation in noisy document collections.

Ultrahigh-Dimensional Multiclass Linear Discriminant Analysis by Pairwise Sure Independence Screening

PubMed Central

Pan, Rui; Wang, Hansheng; Li, Runze

2016-01-01

This paper is concerned with the problem of feature screening for multi-class linear discriminant analysis under ultrahigh dimensional setting. We allow the number of classes to be relatively large. As a result, the total number of relevant features is larger than usual. This makes the related classification problem much more challenging than the conventional one, where the number of classes is small (very often two). To solve the problem, we propose a novel pairwise sure independence screening method for linear discriminant analysis with an ultrahigh dimensional predictor. The proposed procedure is directly applicable to the situation with many classes. We further prove that the proposed method is screening consistent. Simulation studies are conducted to assess the finite sample performance of the new procedure. We also demonstrate the proposed methodology via an empirical analysis of a real life example on handwritten Chinese character recognition. PMID:28127109

Protocol Handbook,

DTIC Science & Technology

1985-04-01

all invitations should be handwritten in black ink and addressed in the full name of the husband and wife unless the guest is single. Requesting an...34 is handwritten in black ink . If the reply is by telephone, the number is written directly beneath the R.S.V.P. (or a separate response card may be...styles. The card should be engraved with black ink on excellent quality card stock (usually white or cream in color). Script lettering is the most

Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification.

PubMed

Pereira, Clayton R; Pereira, Danilo R; Rosa, Gustavo H; Albuquerque, Victor H C; Weber, Silke A T; Hook, Christian; Papa, João P

2018-05-01

Parkinson's disease (PD) is considered a degenerative disorder that affects the motor system, which may cause tremors, micrography, and the freezing of gait. Although PD is related to the lack of dopamine, the triggering process of its development is not fully understood yet. In this work, we introduce convolutional neural networks to learn features from images produced by handwritten dynamics, which capture different information during the individual's assessment. Additionally, we make available a dataset composed of images and signal-based data to foster the research related to computer-aided PD diagnosis. The proposed approach was compared against raw data and texture-based descriptors, showing suitable results, mainly in the context of early stage detection, with results nearly to 95%. The analysis of handwritten dynamics using deep learning techniques showed to be useful for automatic Parkinson's disease identification, as well as it can outperform handcrafted features. Copyright © 2018 Elsevier B.V. All rights reserved.

Analysis of line structure in handwritten documents using the Hough transform

NASA Astrophysics Data System (ADS)

Ball, Gregory R.; Kasiviswanathan, Harish; Srihari, Sargur N.; Narayanan, Aswin

2010-01-01

In the analysis of handwriting in documents a central task is that of determining line structure of the text, e.g., number of text lines, location of their starting and end-points, line-width, etc. While simple methods can handle ideal images, real world documents have complexities such as overlapping line structure, variable line spacing, line skew, document skew, noisy or degraded images etc. This paper explores the application of the Hough transform method to handwritten documents with the goal of automatically determining global document line structure in a top-down manner which can then be used in conjunction with a bottom-up method such as connected component analysis. The performance is significantly better than other top-down methods, such as the projection profile method. In addition, we evaluate the performance of skew analysis by the Hough transform on handwritten documents.

Target recognition and phase acquisition by using incoherent digital holographic imaging

NASA Astrophysics Data System (ADS)

Lee, Munseob; Lee, Byung-Tak

2017-05-01

In this study, we proposed the Incoherent Digital Holographic Imaging (IDHI) for recognition and phase information of dedicated target. Although recent development of a number of target recognition techniques such as LIDAR, there have limited success in target discrimination, in part due to low-resolution, low scanning speed, and computation power. In the paper, the proposed system consists of the incoherent light source, such as LED, Michelson interferometer, and digital CCD for acquisition of four phase shifting image. First of all, to compare with relative coherence, we used a source as laser and LED, respectively. Through numerical reconstruction by using the four phase shifting method and Fresnel diffraction method, we recovered the intensity and phase image of USAF resolution target apart from about 1.0m distance. In this experiment, we show 1.2 times improvement in resolution compared to conventional imaging. Finally, to confirm the recognition result of camouflaged targets with the same color from background, we carry out to test holographic imaging in incoherent light. In this result, we showed the possibility of a target detection and recognition that used three dimensional shape and size signatures, numerical distance from phase information of obtained holographic image.

Minimal effects of visual memory training on the auditory performance of adult cochlear implant users

PubMed Central

Oba, Sandra I.; Galvin, John J.; Fu, Qian-Jie

2014-01-01

Auditory training has been shown to significantly improve cochlear implant (CI) users’ speech and music perception. However, it is unclear whether post-training gains in performance were due to improved auditory perception or to generally improved attention, memory and/or cognitive processing. In this study, speech and music perception, as well as auditory and visual memory were assessed in ten CI users before, during, and after training with a non-auditory task. A visual digit span (VDS) task was used for training, in which subjects recalled sequences of digits presented visually. After the VDS training, VDS performance significantly improved. However, there were no significant improvements for most auditory outcome measures (auditory digit span, phoneme recognition, sentence recognition in noise, digit recognition in noise), except for small (but significant) improvements in vocal emotion recognition and melodic contour identification. Post-training gains were much smaller with the non-auditory VDS training than observed in previous auditory training studies with CI users. The results suggest that post-training gains observed in previous studies were not solely attributable to improved attention or memory, and were more likely due to improved auditory perception. The results also suggest that CI users may require targeted auditory training to improve speech and music perception. PMID:23516087

Manual versus automated coding of free-text self-reported medication data in the 45 and Up Study: a validation study.

PubMed

Gnjidic, Danijela; Pearson, Sallie-Anne; Hilmer, Sarah N; Basilakis, Jim; Schaffer, Andrea L; Blyth, Fiona M; Banks, Emily

2015-03-30

Increasingly, automated methods are being used to code free-text medication data, but evidence on the validity of these methods is limited. To examine the accuracy of automated coding of previously keyed in free-text medication data compared with manual coding of original handwritten free-text responses (the 'gold standard'). A random sample of 500 participants (475 with and 25 without medication data in the free-text box) enrolled in the 45 and Up Study was selected. Manual coding involved medication experts keying in free-text responses and coding using Anatomical Therapeutic Chemical (ATC) codes (i.e. chemical substance 7-digit level; chemical subgroup 5-digit; pharmacological subgroup 4-digit; therapeutic subgroup 3-digit). Using keyed-in free-text responses entered by non-experts, the automated approach coded entries using the Australian Medicines Terminology database and assigned corresponding ATC codes. Based on manual coding, 1377 free-text entries were recorded and, of these, 1282 medications were coded to ATCs manually. The sensitivity of automated coding compared with manual coding was 79% (n = 1014) for entries coded at the exact ATC level, and 81.6% (n = 1046), 83.0% (n = 1064) and 83.8% (n = 1074) at the 5, 4 and 3-digit ATC levels, respectively. The sensitivity of automated coding for blank responses was 100% compared with manual coding. Sensitivity of automated coding was highest for prescription medications and lowest for vitamins and supplements, compared with the manual approach. Positive predictive values for automated coding were above 95% for 34 of the 38 individual prescription medications examined. Automated coding for free-text prescription medication data shows very high to excellent sensitivity and positive predictive values, indicating that automated methods can potentially be useful for large-scale, medication-related research.

Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.

PubMed

Spoerer, Courtney J; McClure, Patrick; Kriegeskorte, Nikolaus

2017-01-01

Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in the human and non-human primate brain. Here we investigate recurrent convolutional neural networks with bottom-up (B), lateral (L), and top-down (T) connections. Combining these types of connections yields four architectures (B, BT, BL, and BLT), which we systematically test and compare. We hypothesized that recurrent dynamics might improve recognition performance in the challenging scenario of partial occlusion. We introduce two novel occluded object recognition tasks to test the efficacy of the models, digit clutter (where multiple target digits occlude one another) and digit debris (where target digits are occluded by digit fragments). We find that recurrent neural networks outperform feedforward control models (approximately matched in parametric complexity) at recognizing objects, both in the absence of occlusion and in all occlusion conditions. Recurrent networks were also found to be more robust to the inclusion of additive Gaussian noise. Recurrent neural networks are better in two respects: (1) they are more neurobiologically realistic than their feedforward counterparts; (2) they are better in terms of their ability to recognize objects, especially under challenging conditions. This work shows that computer vision can benefit from using recurrent convolutional architectures and suggests that the ubiquitous recurrent connections in biological brains are essential for task performance.

Archive of digital Boomer seismic reflection data collected during USGS Cruises 94CCT01 and 95CCT01, eastern Texas and western Louisiana, 1994 and 1995

USGS Publications Warehouse

Calderon, Karynna; Dadisman, Shawn V.; Kindinger, Jack G.; Flocks, James G.; Morton, Robert A.; Wiese, Dana S.

2004-01-01

In June of 1994 and August and September of 1995, the U.S. Geological Survey, in cooperation with the University of Texas Bureau of Economic Geology, conducted geophysical surveys of the Sabine and Calcasieu Lake areas and the Gulf of Mexico offshore eastern Texas and western Louisiana. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, observers' logbooks, GIS information, and formal FGDC metadata. In addition, a filtered and gained GIF image of each seismic profile is provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Examples of SU processing scripts and in-house (USGS) software for viewing SEG-Y files (Zihlman, 1992) are also provided. Processed profile images, trackline maps, navigation files, and formal metadata may be viewed with a web browser. Scanned handwritten logbooks and Field Activity Collection System (FACS) logs may be viewed with Adobe Reader.

Archive of digital Boomer and Chirp seismic reflection data collected during USGS Cruises 01RCE05 and 02RCE01 in the Lower Atchafalaya River, Mississippi River Delta, and offshore southeastern Louisiana, October 23-30, 2001, and August 18-19, 2002

USGS Publications Warehouse

Calderon, Karynna; Dadisman, Shawn V.; Kindinger, Jack G.; Flocks, James G.; Ferina, Nicholas F.; Wiese, Dana S.

2004-01-01

In October of 2001 and August of 2002, the U.S. Geological Survey conducted geophysical surveys of the Lower Atchafalaya River, the Mississippi River Delta, Barataria Bay, and the Gulf of Mexico south of East Timbalier Island, Louisiana. This report serves as an archive of unprocessed digital marine seismic reflection data, trackline maps, navigation files, observers' logbooks, GIS information, and formal FGDC metadata. In addition, a filtered and gained GIF image of each seismic profile is provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and othes, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Examples of SU processing scripts and in-house (USGS) software for viewing SEG-Y files (Zihlman, 1992) are also provided. Processed profile images, trackline maps, navigation files, and formal metadata may be viewed with a web browser. Scanned handwritten logbooks and Field Activity Collection System (FACS) logs may be viewed with Adobe Reader.

Towards fraud-proof ID documents using multiple data hiding technologies and biometrics

NASA Astrophysics Data System (ADS)

Picard, Justin; Vielhauer, Claus; Thorwirth, Niels

2004-06-01

Identity documents, such as ID cards, passports, and driver's licenses, contain textual information, a portrait of the legitimate holder, and eventually some other biometric characteristics such as a fingerprint or handwritten signature. As prices for digital imaging technologies fall, making them more widely available, we have seen an exponential increase in the ease and the number of counterfeiters that can effectively forge documents. Today, with only limited knowledge of technology and a small amount of money, a counterfeiter can effortlessly replace a photo or modify identity information on a legitimate document to the extent that it is very diffcult to differentiate from the original. This paper proposes a virtually fraud-proof ID document based on a combination of three different data hiding technologies: digital watermarking, 2-D bar codes, and Copy Detection Pattern, plus additional biometric protection. As will be shown, that combination of data hiding technologies protects the document against any forgery, in principle without any requirement for other security features. To prevent a genuine document to be used by an illegitimate user,biometric information is also covertly stored in the ID document, to be used for identification at the detector.

Fuzzy logic and neural networks in artificial intelligence and pattern recognition

NASA Astrophysics Data System (ADS)

Sanchez, Elie

1991-10-01

With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.

A Limited-Vocabulary, Multi-Speaker Automatic Isolated Word Recognition System.

ERIC Educational Resources Information Center

Paul, James E., Jr.

Techniques for automatic recognition of isolated words are investigated, and a computer simulation of a word recognition system is effected. Considered in detail are data acquisition and digitizing, word detection, amplitude and time normalization, short-time spectral estimation including spectral windowing, spectral envelope approximation,…

Implementation theory of distortion-invariant pattern recognition for optical and digital signal processing systems

NASA Astrophysics Data System (ADS)

Lhamon, Michael Earl

A pattern recognition system which uses complex correlation filter banks requires proportionally more computational effort than single-real valued filters. This introduces increased computation burden but also introduces a higher level of parallelism, that common computing platforms fail to identify. As a result, we consider algorithm mapping to both optical and digital processors. For digital implementation, we develop computationally efficient pattern recognition algorithms, referred to as, vector inner product operators that require less computational effort than traditional fast Fourier methods. These algorithms do not need correlation and they map readily onto parallel digital architectures, which imply new architectures for optical processors. These filters exploit circulant-symmetric matrix structures of the training set data representing a variety of distortions. By using the same mathematical basis as with the vector inner product operations, we are able to extend the capabilities of more traditional correlation filtering to what we refer to as "Super Images". These "Super Images" are used to morphologically transform a complicated input scene into a predetermined dot pattern. The orientation of the dot pattern is related to the rotational distortion of the object of interest. The optical implementation of "Super Images" yields feature reduction necessary for using other techniques, such as artificial neural networks. We propose a parallel digital signal processor architecture based on specific pattern recognition algorithms but general enough to be applicable to other similar problems. Such an architecture is classified as a data flow architecture. Instead of mapping an algorithm to an architecture, we propose mapping the DSP architecture to a class of pattern recognition algorithms. Today's optical processing systems have difficulties implementing full complex filter structures. Typically, optical systems (like the 4f correlators) are limited to phase-only implementation with lower detection performance than full complex electronic systems. Our study includes pseudo-random pixel encoding techniques for approximating full complex filtering. Optical filter bank implementation is possible and they have the advantage of time averaging the entire filter bank at real time rates. Time-averaged optical filtering is computational comparable to billions of digital operations-per-second. For this reason, we believe future trends in high speed pattern recognition will involve hybrid architectures of both optical and DSP elements.

Modeling the Lexical Morphology of Western Handwritten Signatures

PubMed Central

Diaz-Cabrera, Moises; Ferrer, Miguel A.; Morales, Aythami

2015-01-01

A handwritten signature is the final response to a complex cognitive and neuromuscular process which is the result of the learning process. Because of the many factors involved in signing, it is possible to study the signature from many points of view: graphologists, forensic experts, neurologists and computer vision experts have all examined them. Researchers study written signatures for psychiatric, penal, health and automatic verification purposes. As a potentially useful, multi-purpose study, this paper is focused on the lexical morphology of handwritten signatures. This we understand to mean the identification, analysis, and description of the signature structures of a given signer. In this work we analyze different public datasets involving 1533 signers from different Western geographical areas. Some relevant characteristics of signature lexical morphology have been selected, examined in terms of their probability distribution functions and modeled through a General Extreme Value distribution. This study suggests some useful models for multi-disciplinary sciences which depend on handwriting signatures. PMID:25860942

Script-independent text line segmentation in freestyle handwritten documents.

PubMed

Li, Yi; Zheng, Yefeng; Doermann, David; Jaeger, Stefan; Li, Yi

2008-08-01

Text line segmentation in freestyle handwritten documents remains an open document analysis problem. Curvilinear text lines and small gaps between neighboring text lines present a challenge to algorithms developed for machine printed or hand-printed documents. In this paper, we propose a novel approach based on density estimation and a state-of-the-art image segmentation technique, the level set method. From an input document image, we estimate a probability map, where each element represents the probability that the underlying pixel belongs to a text line. The level set method is then exploited to determine the boundary of neighboring text lines by evolving an initial estimate. Unlike connected component based methods ( [1], [2] for example), the proposed algorithm does not use any script-specific knowledge. Extensive quantitative experiments on freestyle handwritten documents with diverse scripts, such as Arabic, Chinese, Korean, and Hindi, demonstrate that our algorithm consistently outperforms previous methods [1]-[3]. Further experiments show the proposed algorithm is robust to scale change, rotation, and noise.

A New Approach to Diagnose Parkinson's Disease Using a Structural Cooccurrence Matrix for a Similarity Analysis.

PubMed

de Souza, João W M; Alves, Shara S A; Rebouças, Elizângela de S; Almeida, Jefferson S; Rebouças Filho, Pedro P

2018-01-01

Parkinson's disease affects millions of people around the world and consequently various approaches have emerged to help diagnose this disease, among which we can highlight handwriting exams. Extracting features from handwriting exams is an important contribution of the computational field for the diagnosis of this disease. In this paper, we propose an approach that measures the similarity between the exam template and the handwritten trace of the patient following the exam template. This similarity was measured using the Structural Cooccurrence Matrix to calculate how close the handwritten trace of the patient is to the exam template. The proposed approach was evaluated using various exam templates and the handwritten traces of the patient. Each of these variations was used together with the Naïve Bayes, OPF, and SVM classifiers. In conclusion the proposed approach was proven to be better than the existing methods found in the literature and is therefore a promising tool for the diagnosis of Parkinson's disease.

CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arendt, Dustin L.; Komurlu, Caner; Blaha, Leslie M.

We developed CHISSL, a human-machine interface that utilizes supervised machine learning in an unsupervised context to help the user group unlabeled instances by her own mental model. The user primarily interacts via correction (moving a misplaced instance into its correct group) or confirmation (accepting that an instance is placed in its correct group). Concurrent with the user's interactions, CHISSL trains a classification model guided by the user's grouping of the data. It then predicts the group of unlabeled instances and arranges some of these alongside the instances manually organized by the user. We hypothesize that this mode of human andmore » machine collaboration is more effective than Active Learning, wherein the machine decides for itself which instances should be labeled by the user. We found supporting evidence for this hypothesis in a pilot study where we applied CHISSL to organize a collection of handwritten digits.« less

[The electronic health record: computerised provider order entry and the electronic instruction document as new functionalities].

PubMed

Derikx, Joep P M; Erdkamp, Frans L G; Hoofwijk, A G M

2013-01-01

An electronic health record (EHR) should provide 4 key functionalities: (a) documenting patient data; (b) facilitating computerised provider order entry; (c) displaying the results of diagnostic research; and (d) providing support for healthcare providers in the clinical decision-making process.- Computerised provider order entry into the EHR enables the electronic receipt and transfer of orders to ancillary departments, which can take the place of handwritten orders.- By classifying the computer provider order entries according to disorders, digital care pathways can be created. Such care pathways could result in faster and improved diagnostics.- Communicating by means of an electronic instruction document that is linked to a computerised provider order entry facilitates the provision of healthcare in a safer, more efficient and auditable manner.- The implementation of a full-scale EHR has been delayed as a result of economic, technical and legal barriers, as well as some resistance by physicians.

Flexible Kernel Memory

PubMed Central

Nowicki, Dimitri; Siegelmann, Hava

2010-01-01

This paper introduces a new model of associative memory, capable of both binary and continuous-valued inputs. Based on kernel theory, the memory model is on one hand a generalization of Radial Basis Function networks and, on the other, is in feature space, analogous to a Hopfield network. Attractors can be added, deleted, and updated on-line simply, without harming existing memories, and the number of attractors is independent of input dimension. Input vectors do not have to adhere to a fixed or bounded dimensionality; they can increase and decrease it without relearning previous memories. A memory consolidation process enables the network to generalize concepts and form clusters of input data, which outperforms many unsupervised clustering techniques; this process is demonstrated on handwritten digits from MNIST. Another process, reminiscent of memory reconsolidation is introduced, in which existing memories are refreshed and tuned with new inputs; this process is demonstrated on series of morphed faces. PMID:20552013

Offline Signature Verification Using the Discrete Radon Transform and a Hidden Markov Model

NASA Astrophysics Data System (ADS)

Coetzer, J.; Herbst, B. M.; du Preez, J. A.

2004-12-01

We developed a system that automatically authenticates offline handwritten signatures using the discrete Radon transform (DRT) and a hidden Markov model (HMM). Given the robustness of our algorithm and the fact that only global features are considered, satisfactory results are obtained. Using a database of 924 signatures from 22 writers, our system achieves an equal error rate (EER) of 18% when only high-quality forgeries (skilled forgeries) are considered and an EER of 4.5% in the case of only casual forgeries. These signatures were originally captured offline. Using another database of 4800 signatures from 51 writers, our system achieves an EER of 12.2% when only skilled forgeries are considered. These signatures were originally captured online and then digitally converted into static signature images. These results compare well with the results of other algorithms that consider only global features.

Latent log-linear models for handwritten digit classification.

PubMed

Deselaers, Thomas; Gass, Tobias; Heigold, Georg; Ney, Hermann

2012-06-01

We present latent log-linear models, an extension of log-linear models incorporating latent variables, and we propose two applications thereof: log-linear mixture models and image deformation-aware log-linear models. The resulting models are fully discriminative, can be trained efficiently, and the model complexity can be controlled. Log-linear mixture models offer additional flexibility within the log-linear modeling framework. Unlike previous approaches, the image deformation-aware model directly considers image deformations and allows for a discriminative training of the deformation parameters. Both are trained using alternating optimization. For certain variants, convergence to a stationary point is guaranteed and, in practice, even variants without this guarantee converge and find models that perform well. We tune the methods on the USPS data set and evaluate on the MNIST data set, demonstrating the generalization capabilities of our proposed models. Our models, although using significantly fewer parameters, are able to obtain competitive results with models proposed in the literature.

Attention and recognition memory bias for alcohol-related stimuli among alcohol-dependent patients attending residential treatment.

PubMed

Klein, Audrey A; Nelson, Lindsay M; Anker, Justin J

2013-03-01

Though studies have examined attentional bias for alcohol-related information among alcohol-dependent individuals, few have examined memory bias. This study examined attention and recognition memory biases for alcohol-related information among patients recently admitted to residential alcohol treatment (n=100; 40% female). Participants completed a computerized attentional task wherein they classified a centrally-presented digit as odd or even. On some trials, an alcohol word, neutral word, or anagram was presented along with the digit. On these dual trials participants first classified the digit and then classified the other stimulus as a word or nonword. Participants took longer to classify digits that appeared with alcohol words compared to neutral words; suggesting the alcohol words distracted them from processing the digit. In a subsequent recognition memory test, participants showed significantly higher hit rates (i.e., correctly classifying an old item as old) and false alarm rates (i.e., incorrectly classifying a new item as old) to the alcohol words compared to the neutral words, and they also showed a more liberal response bias to alcohol words. The findings suggest that alcohol-dependent individuals exhibit both attention and memory bias for alcohol-related information. Copyright © 2012 Elsevier Ltd. All rights reserved.

Military applications of automatic speech recognition and future requirements

NASA Technical Reports Server (NTRS)

Beek, Bruno; Cupples, Edward J.

1977-01-01

An updated summary of the state-of-the-art of automatic speech recognition and its relevance to military applications is provided. A number of potential systems for military applications are under development. These include: (1) digital narrowband communication systems; (2) automatic speech verification; (3) on-line cartographic processing unit; (4) word recognition for militarized tactical data system; and (5) voice recognition and synthesis for aircraft cockpit.

Neural networks and applications tutorial

NASA Astrophysics Data System (ADS)

Guyon, I.

1991-09-01

The importance of neural networks has grown dramatically during this decade. While only a few years ago they were primarily of academic interest, now dozens of companies and many universities are investigating the potential use of these systems and products are beginning to appear. The idea of building a machine whose architecture is inspired by that of the brain has roots which go far back in history. Nowadays, technological advances of computers and the availability of custom integrated circuits, permit simulations of hundreds or even thousands of neurons. In conjunction, the growing interest in learning machines, non-linear dynamics and parallel computation spurred renewed attention in artificial neural networks. Many tentative applications have been proposed, including decision systems (associative memories, classifiers, data compressors and optimizers), or parametric models for signal processing purposes (system identification, automatic control, noise canceling, etc.). While they do not always outperform standard methods, neural network approaches are already used in some real world applications for pattern recognition and signal processing tasks. The tutorial is divided into six lectures, that where presented at the Third Graduate Summer Course on Computational Physics (September 3-7, 1990) on Parallel Architectures and Applications, organized by the European Physical Society: (1) Introduction: machine learning and biological computation. (2) Adaptive artificial neurons (perceptron, ADALINE, sigmoid units, etc.): learning rules and implementations. (3) Neural network systems: architectures, learning algorithms. (4) Applications: pattern recognition, signal processing, etc. (5) Elements of learning theory: how to build networks which generalize. (6) A case study: a neural network for on-line recognition of handwritten alphanumeric characters.

Rapid Naming Speed and Chinese Character Recognition

ERIC Educational Resources Information Center

Liao, Chen-Huei; Georgiou, George K.; Parrila, Rauno

2008-01-01

We examined the relationship between rapid naming speed (RAN) and Chinese character recognition accuracy and fluency. Sixty-three grade 2 and 54 grade 4 Taiwanese children were administered four RAN tasks (colors, digits, Zhu-Yin-Fu-Hao, characters), and two character recognition tasks. RAN tasks accounted for more reading variance in grade 4 than…

A Signal Detection Analysis of Digitized and Photographic Image Modes and Color Realism in a Pictorial Recognition Memory Task.

ERIC Educational Resources Information Center

El-Gazzar, Abdel-Latif I.

The relative effectiveness of digital versus photographic images was examined with 96 college students as subjects. A 2x2 balanced factorial design was employed to test eight hypotheses. The four groups were (1) digitized black and white; (2) digitized pseudocolor; (3) photographic black and white; and (4) photographic realistic color. Findings…

A Record Book of Open Heart Surgical Cases between 1959 and 1982, Hand-Written by a Cardiac Surgeon.

PubMed

Kim, Won-Gon

2016-08-01

A book of brief records of open heart surgery underwent between 1959 and 1982 at Seoul National University Hospital was recently found. The book was hand-written by the late professor and cardiac surgeon Yung Kyoon Lee (1921-1994). This book contains valuable information about cardiac patients and surgery at the early stages of the establishment of open heart surgery in Korea, and at Seoul National University Hospital. This report is intended to analyze the content of the book.

A Taxonomy of 3D Occluded Objects Recognition Techniques

NASA Astrophysics Data System (ADS)

Soleimanizadeh, Shiva; Mohamad, Dzulkifli; Saba, Tanzila; Al-ghamdi, Jarallah Saleh

2016-03-01

The overall performances of object recognition techniques under different condition (e.g., occlusion, viewpoint, and illumination) have been improved significantly in recent years. New applications and hardware are shifted towards digital photography, and digital media. This faces an increase in Internet usage requiring object recognition for certain applications; particularly occulded objects. However occlusion is still an issue unhandled, interlacing the relations between extracted feature points through image, research is going on to develop efficient techniques and easy to use algorithms that would help users to source images; this need to overcome problems and issues regarding occlusion. The aim of this research is to review recognition occluded objects algorithms and figure out their pros and cons to solve the occlusion problem features, which are extracted from occluded object to distinguish objects from other co-existing objects by determining the new techniques, which could differentiate the occluded fragment and sections inside an image.

Analysis of objects in binary images. M.S. Thesis - Old Dominion Univ.

NASA Technical Reports Server (NTRS)

Leonard, Desiree M.

1991-01-01

Digital image processing techniques are typically used to produce improved digital images through the application of successive enhancement techniques to a given image or to generate quantitative data about the objects within that image. In support of and to assist researchers in a wide range of disciplines, e.g., interferometry, heavy rain effects on aerodynamics, and structure recognition research, it is often desirable to count objects in an image and compute their geometric properties. Therefore, an image analysis application package, focusing on a subset of image analysis techniques used for object recognition in binary images, was developed. This report describes the techniques and algorithms utilized in three main phases of the application and are categorized as: image segmentation, object recognition, and quantitative analysis. Appendices provide supplemental formulas for the algorithms employed as well as examples and results from the various image segmentation techniques and the object recognition algorithm implemented.

Arabic writer identification based on diacritic's features

NASA Astrophysics Data System (ADS)

Maliki, Makki; Al-Jawad, Naseer; Jassim, Sabah A.

2012-06-01

Natural languages like Arabic, Kurdish, Farsi (Persian), Urdu, and any other similar languages have many features, which make them different from other languages like Latin's script. One of these important features is diacritics. These diacritics are classified as: compulsory like dots which are used to identify/differentiate letters, and optional like short vowels which are used to emphasis consonants. Most indigenous and well trained writers often do not use all or some of these second class of diacritics, and expert readers can infer their presence within the context of the writer text. In this paper, we investigate the use of diacritics shapes and other characteristic as parameters of feature vectors for Arabic writer identification/verification. Segmentation techniques are used to extract the diacritics-based feature vectors from examples of Arabic handwritten text. The results of evaluation test will be presented, which has been carried out on an in-house database of 50 writers. Also the viability of using diacritics for writer recognition will be demonstrated.

SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations

NASA Astrophysics Data System (ADS)

Choi, Shinhyun; Tan, Scott H.; Li, Zefan; Kim, Yunjo; Choi, Chanyeol; Chen, Pai-Yu; Yeon, Hanwool; Yu, Shimeng; Kim, Jeehwan

2018-01-01

Although several types of architecture combining memory cells and transistors have been used to demonstrate artificial synaptic arrays, they usually present limited scalability and high power consumption. Transistor-free analog switching devices may overcome these limitations, yet the typical switching process they rely on—formation of filaments in an amorphous medium—is not easily controlled and hence hampers the spatial and temporal reproducibility of the performance. Here, we demonstrate analog resistive switching devices that possess desired characteristics for neuromorphic computing networks with minimal performance variations using a single-crystalline SiGe layer epitaxially grown on Si as a switching medium. Such epitaxial random access memories utilize threading dislocations in SiGe to confine metal filaments in a defined, one-dimensional channel. This confinement results in drastically enhanced switching uniformity and long retention/high endurance with a high analog on/off ratio. Simulations using the MNIST handwritten recognition data set prove that epitaxial random access memories can operate with an online learning accuracy of 95.1%.

Automated Categorization Scheme for Digital Libraries in Distance Learning: A Pattern Recognition Approach

ERIC Educational Resources Information Center

Gunal, Serkan

2008-01-01

Digital libraries play a crucial role in distance learning. Nowadays, they are one of the fundamental information sources for the students enrolled in this learning system. These libraries contain huge amount of instructional data (text, audio and video) offered by the distance learning program. Organization of the digital libraries is…

Context-dependent similarity effects in letter recognition.

PubMed

Kinoshita, Sachiko; Robidoux, Serje; Guilbert, Daniel; Norris, Dennis

2015-10-01

In visual word recognition tasks, digit primes that are visually similar to letter string targets (e.g., 4/A, 8/B) are known to facilitate letter identification relative to visually dissimilar digits (e.g., 6/A, 7/B); in contrast, with letter primes, visual similarity effects have been elusive. In the present study we show that the visual similarity effect with letter primes can be made to come and go, depending on whether it is necessary to discriminate between visually similar letters. The results support a Bayesian view which regards letter recognition not as a passive activation process driven by the fixed stimulus properties, but as a dynamic evidence accumulation process for a decision that is guided by the task context.

SharedCanvas: A Collaborative Model for Medieval Manuscript Layout Dissemination

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sanderson, Robert D.; Albritton, Benjamin; Schwemmer, Rafael

2011-01-01

In this paper we present a model based on the principles of Linked Data that can be used to describe the interrelationships of images, texts and other resources to facilitate the interoperability of repositories of medieval manuscripts or other culturally important handwritten documents. The model is designed from a set of requirements derived from the real world use cases of some of the largest digitized medieval content holders, and instantiations of the model are intended as the input to collection-independent page turning and scholarly presentation interfaces. A canvas painting paradigm, such as in PDF and SVG, was selected based onmore » the lack of a one to one correlation between image and page, and to fulfill complex requirements such as when the full text of a page is known, but only fragments of the physical object remain. The model is implemented using technologies such as OAI-ORE Aggregations and OAC Annotations, as the fundamental building blocks of emerging Linked Digital Libraries. The model and implementation are evaluated through prototypes of both content providing and consuming applications. Although the system was designed from requirements drawn from the medieval manuscript domain, it is applicable to any layout-oriented presentation of images of text.« less

A natural approach to convey numerical digits using hand activity recognition based on hand shape features

NASA Astrophysics Data System (ADS)

Chidananda, H.; Reddy, T. Hanumantha

2017-06-01

This paper presents a natural representation of numerical digit(s) using hand activity analysis based on number of fingers out stretched for each numerical digit in sequence extracted from a video. The analysis is based on determining a set of six features from a hand image. The most important features used from each frame in a video are the first fingertip from top, palm-line, palm-center, valley points between the fingers exists above the palm-line. Using this work user can convey any number of numerical digits using right or left or both the hands naturally in a video. Each numerical digit ranges from 0 to9. Hands (right/left/both) used to convey digits can be recognized accurately using the valley points and with this recognition whether the user is a right / left handed person in practice can be analyzed. In this work, first the hand(s) and face parts are detected by using YCbCr color space and face part is removed by using ellipse based method. Then, the hand(s) are analyzed to recognize the activity that represents a series of numerical digits in a video. This work uses pixel continuity algorithm using 2D coordinate geometry system and does not use regular use of calculus, contours, convex hull and datasets.

Exhibits Recognition System for Combining Online Services and Offline Services

NASA Astrophysics Data System (ADS)

Ma, He; Liu, Jianbo; Zhang, Yuan; Wu, Xiaoyu

2017-10-01

In order to achieve a more convenient and accurate digital museum navigation, we have developed a real-time and online-to-offline museum exhibits recognition system using image recognition method based on deep learning. In this paper, the client and server of the system are separated and connected through the HTTP. Firstly, by using the client app in the Android mobile phone, the user can take pictures and upload them to the server. Secondly, the features of the picture are extracted using the deep learning network in the server. With the help of the features, the pictures user uploaded are classified with a well-trained SVM. Finally, the classification results are sent to the client and the detailed exhibition’s introduction corresponding to the classification results are shown in the client app. Experimental results demonstrate that the recognition accuracy is close to 100% and the computing time from the image uploading to the exhibit information show is less than 1S. By means of exhibition image recognition algorithm, our implemented exhibits recognition system can combine online detailed exhibition information to the user in the offline exhibition hall so as to achieve better digital navigation.

Multi-font printed Mongolian document recognition system

NASA Astrophysics Data System (ADS)

Peng, Liangrui; Liu, Changsong; Ding, Xiaoqing; Wang, Hua; Jin, Jianming

2009-01-01

Mongolian is one of the major ethnic languages in China. Large amount of Mongolian printed documents need to be digitized in digital library and various applications. Traditional Mongolian script has unique writing style and multi-font-type variations, which bring challenges to Mongolian OCR research. As traditional Mongolian script has some characteristics, for example, one character may be part of another character, we define the character set for recognition according to the segmented components, and the components are combined into characters by rule-based post-processing module. For character recognition, a method based on visual directional feature and multi-level classifiers is presented. For character segmentation, a scheme is used to find the segmentation point by analyzing the properties of projection and connected components. As Mongolian has different font-types which are categorized into two major groups, the parameter of segmentation is adjusted for each group. A font-type classification method for the two font-type group is introduced. For recognition of Mongolian text mixed with Chinese and English, language identification and relevant character recognition kernels are integrated. Experiments show that the presented methods are effective. The text recognition rate is 96.9% on the test samples from practical documents with multi-font-types and mixed scripts.

Group discriminatory power of handwritten characters

NASA Astrophysics Data System (ADS)

Tomai, Catalin I.; Kshirsagar, Devika M.; Srihari, Sargur N.

2003-12-01

Using handwritten characters we address two questions (i) what is the group identification performance of different alphabets (upper and lower case) and (ii) what are the best characters for the verification task (same writer/different writer discrimination) knowing demographic information about the writer such as ethnicity, age or sex. The Bhattacharya distance is used to rank different characters by their group discriminatory power and the k-nn classifier to measure the individual performance of characters for group identification. Given the tasks of identifying the correct gender/age/ethnicity or handedness, the accumulated performance of characters varies between 65% and 85%.

Terrain type recognition using ERTS-1 MSS images

NASA Technical Reports Server (NTRS)

Gramenopoulos, N.

1973-01-01

For the automatic recognition of earth resources from ERTS-1 digital tapes, both multispectral and spatial pattern recognition techniques are important. Recognition of terrain types is based on spatial signatures that become evident by processing small portions of an image through selected algorithms. An investigation of spatial signatures that are applicable to ERTS-1 MSS images is described. Artifacts in the spatial signatures seem to be related to the multispectral scanner. A method for suppressing such artifacts is presented. Finally, results of terrain type recognition for one ERTS-1 image are presented.

Processing Electromyographic Signals to Recognize Words

NASA Technical Reports Server (NTRS)

Jorgensen, C. C.; Lee, D. D.

2009-01-01

A recently invented speech-recognition method applies to words that are articulated by means of the tongue and throat muscles but are otherwise not voiced or, at most, are spoken sotto voce. This method could satisfy a need for speech recognition under circumstances in which normal audible speech is difficult, poses a hazard, is disturbing to listeners, or compromises privacy. The method could also be used to augment traditional speech recognition by providing an additional source of information about articulator activity. The method can be characterized as intermediate between (1) conventional speech recognition through processing of voice sounds and (2) a method, not yet developed, of processing electroencephalographic signals to extract unspoken words directly from thoughts. This method involves computational processing of digitized electromyographic (EMG) signals from muscle innervation acquired by surface electrodes under a subject's chin near the tongue and on the side of the subject s throat near the larynx. After preprocessing, digitization, and feature extraction, EMG signals are processed by a neural-network pattern classifier, implemented in software, that performs the bulk of the recognition task as described.

The effects of digital signal processing features on children's speech recognition and loudness perception.

PubMed

Crukley, Jeffery; Scollie, Susan D

2014-03-01

The purpose of this study was to determine the effects of hearing instruments set to Desired Sensation Level version 5 (DSL v5) hearing instrument prescription algorithm targets and equipped with directional microphones and digital noise reduction (DNR) on children's sentence recognition in noise performance and loudness perception in a classroom environment. Ten children (ages 8-17 years) with stable, congenital sensorineural hearing losses participated in the study. Participants were fitted bilaterally with behind-the-ear hearing instruments set to DSL v5 prescriptive targets. Sentence recognition in noise was evaluated using the Bamford-Kowal-Bench Speech in Noise Test (Niquette et al., 2003). Loudness perception was evaluated using a modified version of the Contour Test of Loudness Perception (Cox, Alexander, Taylor, & Gray, 1997). Children's sentence recognition in noise performance was significantly better when using directional microphones alone or in combination with DNR than when using omnidirectional microphones alone or in combination with DNR. Children's loudness ratings for sounds above 72 dB SPL were lowest when fitted with the DSL v5 Noise prescription combined with directional microphones. DNR use showed no effect on loudness ratings. Use of the DSL v5 Noise prescription with a directional microphone improved sentence recognition in noise performance and reduced loudness perception ratings for loud sounds relative to a typical clinical reference fitting with the DSL v5 Quiet prescription with no digital signal processing features enabled. Potential clinical strategies are discussed.

Ventral-stream-like shape representation: from pixel intensity values to trainable object-selective COSFIRE models

PubMed Central

Azzopardi, George; Petkov, Nicolai

2014-01-01

The remarkable abilities of the primate visual system have inspired the construction of computational models of some visual neurons. We propose a trainable hierarchical object recognition model, which we call S-COSFIRE (S stands for Shape and COSFIRE stands for Combination Of Shifted FIlter REsponses) and use it to localize and recognize objects of interests embedded in complex scenes. It is inspired by the visual processing in the ventral stream (V1/V2 → V4 → TEO). Recognition and localization of objects embedded in complex scenes is important for many computer vision applications. Most existing methods require prior segmentation of the objects from the background which on its turn requires recognition. An S-COSFIRE filter is automatically configured to be selective for an arrangement of contour-based features that belong to a prototype shape specified by an example. The configuration comprises selecting relevant vertex detectors and determining certain blur and shift parameters. The response is computed as the weighted geometric mean of the blurred and shifted responses of the selected vertex detectors. S-COSFIRE filters share similar properties with some neurons in inferotemporal cortex, which provided inspiration for this work. We demonstrate the effectiveness of S-COSFIRE filters in two applications: letter and keyword spotting in handwritten manuscripts and object spotting in complex scenes for the computer vision system of a domestic robot. S-COSFIRE filters are effective to recognize and localize (deformable) objects in images of complex scenes without requiring prior segmentation. They are versatile trainable shape detectors, conceptually simple and easy to implement. The presented hierarchical shape representation contributes to a better understanding of the brain and to more robust computer vision algorithms. PMID:25126068

Working and strategic memory deficits in schizophrenia

NASA Technical Reports Server (NTRS)

Stone, M.; Gabrieli, J. D.; Stebbins, G. T.; Sullivan, E. V.

1998-01-01

Working memory and its contribution to performance on strategic memory tests in schizophrenia were studied. Patients (n = 18) and control participants (n = 15), all men, received tests of immediate memory (forward digit span), working memory (listening, computation, and backward digit span), and long-term strategic (free recall, temporal order, and self-ordered pointing) and nonstrategic (recognition) memory. Schizophrenia patients performed worse on all tests. Education, verbal intelligence, and immediate memory capacity did not account for deficits in working memory in schizophrenia patients. Reduced working memory capacity accounted for group differences in strategic memory but not in recognition memory. Working memory impairment may be central to the profile of impaired cognitive performance in schizophrenia and is consistent with hypothesized frontal lobe dysfunction associated with this disease. Additional medial-temporal dysfunction may account for the recognition memory deficit.

A Digital Liquid State Machine With Biologically Inspired Learning and Its Application to Speech Recognition.

PubMed

Zhang, Yong; Li, Peng; Jin, Yingyezhe; Choe, Yoonsuck

2015-11-01

This paper presents a bioinspired digital liquid-state machine (LSM) for low-power very-large-scale-integration (VLSI)-based machine learning applications. To the best of the authors' knowledge, this is the first work that employs a bioinspired spike-based learning algorithm for the LSM. With the proposed online learning, the LSM extracts information from input patterns on the fly without needing intermediate data storage as required in offline learning methods such as ridge regression. The proposed learning rule is local such that each synaptic weight update is based only upon the firing activities of the corresponding presynaptic and postsynaptic neurons without incurring global communications across the neural network. Compared with the backpropagation-based learning, the locality of computation in the proposed approach lends itself to efficient parallel VLSI implementation. We use subsets of the TI46 speech corpus to benchmark the bioinspired digital LSM. To reduce the complexity of the spiking neural network model without performance degradation for speech recognition, we study the impacts of synaptic models on the fading memory of the reservoir and hence the network performance. Moreover, we examine the tradeoffs between synaptic weight resolution, reservoir size, and recognition performance and present techniques to further reduce the overhead of hardware implementation. Our simulation results show that in terms of isolated word recognition evaluated using the TI46 speech corpus, the proposed digital LSM rivals the state-of-the-art hidden Markov-model-based recognizer Sphinx-4 and outperforms all other reported recognizers including the ones that are based upon the LSM or neural networks.

Word spotting for handwritten documents using Chamfer Distance and Dynamic Time Warping

NASA Astrophysics Data System (ADS)

Saabni, Raid M.; El-Sana, Jihad A.

2011-01-01

A large amount of handwritten historical documents are located in libraries around the world. The desire to access, search, and explore these documents paves the way for a new age of knowledge sharing and promotes collaboration and understanding between human societies. Currently, the indexes for these documents are generated manually, which is very tedious and time consuming. Results produced by state of the art techniques, for converting complete images of handwritten documents into textual representations, are not yet sufficient. Therefore, word-spotting methods have been developed to archive and index images of handwritten documents in order to enable efficient searching within documents. In this paper, we present a new matching algorithm to be used in word-spotting tasks for historical Arabic documents. We present a novel algorithm based on the Chamfer Distance to compute the similarity between shapes of word-parts. Matching results are used to cluster images of Arabic word-parts into different classes using the Nearest Neighbor rule. To compute the distance between two word-part images, the algorithm subdivides each image into equal-sized slices (windows). A modified version of the Chamfer Distance, incorporating geometric gradient features and distance transform data, is used as a similarity distance between the different slices. Finally, the Dynamic Time Warping (DTW) algorithm is used to measure the distance between two images of word-parts. By using the DTW we enabled our system to cluster similar word-parts, even though they are transformed non-linearly due to the nature of handwriting. We tested our implementation of the presented methods using various documents in different writing styles, taken from Juma'a Al Majid Center - Dubai, and obtained encouraging results.

[New discovery of the handwritten draft of Eucharius Rösslin's midwifery textbook Pregnant Women and Midwives Rosengarten and Ps.-Ortlof's Small Book for Women].

PubMed

Kruse, B J

1994-01-01

The author of the famous midwifery text book Der schwangeren Frauen und Hebammen Rosengarten has until now thought to have been Eucharius Rösslin the Elder, in whose name the first printed edition of the work appeared in 1513. According to him, he compiled the text from various sources in the years 1508-1512 at the suggestion of the Duchess Catherine of Brunswick-Luneburg. In the SB und UB Hamburg there is a handwritten preliminary draft of Rosengarten (Cod. med. 801, p. 9-130), dated by the scribe in the year 1494 (this is borne out by watermark analysis). It reproduces the text of Rosengarten without the privilegium, the dedication and the rhyming 'admonition' of the pregnant women and the midwives, as well as the glossary and the illustrative woodcuts almost identically. The printed version of Rosengarten was also expanded by Eucharius Rösslin the Elder with passages among others from Ps.-Ortolfs Frauenbüchlein. The author of this paper was also able to trace a handwritten preliminary draft of Frauenbüchlein, until now unknown, in manuscript 2967 of the Austrian National Library in Vienna. The remark Hic liber pertinet ad Constantinum Roeslin written in the manuscript by a previous owner, and a treatise on syphilis in the hand Eucharius Rösslin the Younger, would indicate that Cod. med. 801 was once in the possession of the Rösslin family. Since Eucharius Rösslin the Elder was born around 1470, and since errors and omissions in Cod. med. 801 indicate that it is a copy of an older text, we are confronted with the question of whether or not the handwritten edition of Rosengarten originates from him or from some other author.

Compact hybrid optoelectrical unit for image processing and recognition

NASA Astrophysics Data System (ADS)

Cheng, Gang; Jin, Guofan; Wu, Minxian; Liu, Haisong; He, Qingsheng; Yuan, ShiFu

1998-07-01

In this paper a compact opto-electric unit (CHOEU) for digital image processing and recognition is proposed. The central part of CHOEU is an incoherent optical correlator, which is realized with a SHARP QA-1200 8.4 inch active matrix TFT liquid crystal display panel which is used as two real-time spatial light modulators for both the input image and reference template. CHOEU can do two main processing works. One is digital filtering; the other is object matching. Using CHOEU an edge-detection operator is realized to extract the edges from the input images. Then the reprocessed images are sent into the object recognition unit for identifying the important targets. A novel template- matching method is proposed for gray-tome image recognition. A positive and negative cycle-encoding method is introduced to realize the absolute difference measurement pixel- matching on a correlator structure simply. The system has god fault-tolerance ability for rotation distortion, Gaussian noise disturbance or information losing. The experiments are given at the end of this paper.

Ancient administrative handwritten documents: X-ray analysis and imaging

PubMed Central

Albertin, F.; Astolfo, A.; Stampanoni, M.; Peccenini, Eva; Hwu, Y.; Kaplan, F.; Margaritondo, G.

2015-01-01

Handwritten characters in administrative antique documents from three centuries have been detected using different synchrotron X-ray imaging techniques. Heavy elements in ancient inks, present even for everyday administrative manuscripts as shown by X-ray fluorescence spectra, produce attenuation contrast. In most cases the image quality is good enough for tomography reconstruction in view of future applications to virtual page-by-page ‘reading’. When attenuation is too low, differential phase contrast imaging can reveal the characters from refractive index effects. The results are potentially important for new information harvesting strategies, for example from the huge Archivio di Stato collection, objective of the Venice Time Machine project. PMID:25723946

Ancient administrative handwritten documents: X-ray analysis and imaging.

PubMed

Albertin, F; Astolfo, A; Stampanoni, M; Peccenini, Eva; Hwu, Y; Kaplan, F; Margaritondo, G

2015-03-01

Handwritten characters in administrative antique documents from three centuries have been detected using different synchrotron X-ray imaging techniques. Heavy elements in ancient inks, present even for everyday administrative manuscripts as shown by X-ray fluorescence spectra, produce attenuation contrast. In most cases the image quality is good enough for tomography reconstruction in view of future applications to virtual page-by-page `reading'. When attenuation is too low, differential phase contrast imaging can reveal the characters from refractive index effects. The results are potentially important for new information harvesting strategies, for example from the huge Archivio di Stato collection, objective of the Venice Time Machine project.

Study on recognition algorithm for paper currency numbers based on neural network

NASA Astrophysics Data System (ADS)

Li, Xiuyan; Liu, Tiegen; Li, Yuanyao; Zhang, Zhongchuan; Deng, Shichao

2008-12-01

Based on the unique characteristic, the paper currency numbers can be put into record and the automatic identification equipment for paper currency numbers is supplied to currency circulation market in order to provide convenience for financial sectors to trace the fiduciary circulation socially and provide effective supervision on paper currency. Simultaneously it is favorable for identifying forged notes, blacklisting the forged notes numbers and solving the major social problems, such as armor cash carrier robbery, money laundering. For the purpose of recognizing the paper currency numbers, a recognition algorithm based on neural network is presented in the paper. Number lines in original paper currency images can be draw out through image processing, such as image de-noising, skew correction, segmentation, and image normalization. According to the different characteristics between digits and letters in serial number, two kinds of classifiers are designed. With the characteristics of associative memory, optimization-compute and rapid convergence, the Discrete Hopfield Neural Network (DHNN) is utilized to recognize the letters; with the characteristics of simple structure, quick learning and global optimum, the Radial-Basis Function Neural Network (RBFNN) is adopted to identify the digits. Then the final recognition results are obtained by combining the two kinds of recognition results in regular sequence. Through the simulation tests, it is confirmed by simulation results that the recognition algorithm of combination of two kinds of recognition methods has such advantages as high recognition rate and faster recognition simultaneously, which is worthy of broad application prospect.

Is handwriting constrained by phonology? Evidence from Stroop tasks with written responses and Chinese characters

PubMed Central

Damian, Markus F.; Qu, Qingqing

2013-01-01

To what extent is handwritten word production based on phonological codes? A few studies conducted in Western languages have recently provided evidence showing that phonology contributes to the retrieval of graphemic properties in written output tasks. Less is known about how orthographic production works in languages with non-alphabetic scripts such as written Chinese. We report a Stroop study in which Chinese participants wrote the color of characters on a digital graphic tablet; characters were either neutral, or homophonic to the target (congruent), or homophonic to an alternative (incongruent). Facilitation was found from congruent homophonic distractors, but only when the homophone shared the same tone with the target. This finding suggests a contribution of phonology to written word production. A second experiment served as a control experiment to exclude the possibility that the effect in Experiment 1 had an exclusively semantic locus. Overall, the findings offer new insight into the relative contribution of phonology to handwriting, particularly in non-Western languages. PMID:24146660

Encoding sensory and motor patterns as time-invariant trajectories in recurrent neural networks

PubMed Central

2018-01-01

Much of the information the brain processes and stores is temporal in nature—a spoken word or a handwritten signature, for example, is defined by how it unfolds in time. However, it remains unclear how neural circuits encode complex time-varying patterns. We show that by tuning the weights of a recurrent neural network (RNN), it can recognize and then transcribe spoken digits. The model elucidates how neural dynamics in cortical networks may resolve three fundamental challenges: first, encode multiple time-varying sensory and motor patterns as stable neural trajectories; second, generalize across relevant spatial features; third, identify the same stimuli played at different speeds—we show that this temporal invariance emerges because the recurrent dynamics generate neural trajectories with appropriately modulated angular velocities. Together our results generate testable predictions as to how recurrent networks may use different mechanisms to generalize across the relevant spatial and temporal features of complex time-varying stimuli. PMID:29537963

Robust continuous clustering

PubMed Central

Shah, Sohil Atul

2017-01-01

Clustering is a fundamental procedure in the analysis of scientific data. It is used ubiquitously across the sciences. Despite decades of research, existing clustering algorithms have limited effectiveness in high dimensions and often require tuning parameters for different domains and datasets. We present a clustering algorithm that achieves high accuracy across multiple domains and scales efficiently to high dimensions and large datasets. The presented algorithm optimizes a smooth continuous objective, which is based on robust statistics and allows heavily mixed clusters to be untangled. The continuous nature of the objective also allows clustering to be integrated as a module in end-to-end feature learning pipelines. We demonstrate this by extending the algorithm to perform joint clustering and dimensionality reduction by efficiently optimizing a continuous global objective. The presented approach is evaluated on large datasets of faces, hand-written digits, objects, newswire articles, sensor readings from the Space Shuttle, and protein expression levels. Our method achieves high accuracy across all datasets, outperforming the best prior algorithm by a factor of 3 in average rank. PMID:28851838

Encoding sensory and motor patterns as time-invariant trajectories in recurrent neural networks.

PubMed

Goudar, Vishwa; Buonomano, Dean V

2018-03-14

Much of the information the brain processes and stores is temporal in nature-a spoken word or a handwritten signature, for example, is defined by how it unfolds in time. However, it remains unclear how neural circuits encode complex time-varying patterns. We show that by tuning the weights of a recurrent neural network (RNN), it can recognize and then transcribe spoken digits. The model elucidates how neural dynamics in cortical networks may resolve three fundamental challenges: first, encode multiple time-varying sensory and motor patterns as stable neural trajectories; second, generalize across relevant spatial features; third, identify the same stimuli played at different speeds-we show that this temporal invariance emerges because the recurrent dynamics generate neural trajectories with appropriately modulated angular velocities. Together our results generate testable predictions as to how recurrent networks may use different mechanisms to generalize across the relevant spatial and temporal features of complex time-varying stimuli. © 2018, Goudar et al.

Experiences of Nursing Personnel Using PDAs in Home Health Care Services in Norwegian Municipalities

PubMed Central

Hansen, Linda M.; Fossum, Mariann; Söderhamn, Olle; Fruhling, Ann

2012-01-01

Although nursing personnel have used personal digital assistants (PDAs) to support home health care services for the past ten years, little is known about their experiences. This study was conducted to examine experiences of nursing personnel using a specialized home health care computer software application called Gerica. In addition, this research analyzed how well this application aligned with the workflow of the nursing personnel in their daily care of patients. The evaluation methods included user observations and learnability testing. Nursing personnel from two different municipalities were observed while performing real tasks in natural settings. This study shows that the nursing personnel were satisfied with the PDA user interface and the Gerica software; however, they identified areas for improvement. For example, the nursing personnel were concerned about trusting the reliability of the PDA in order to eliminate the need for handwritten documentation. Solutions to meet these shortcomings for nursing managers and vendors are discussed. PMID:24199073

Measures of Working Memory Span and Verbal Rehearsal Speed in Deaf Children after Cochlear Implantation

PubMed Central

Pisoni, David B.; Cleary, Miranda

2012-01-01

Large individual differences in spoken word recognition performance have been found in deaf children after cochlear implantation. Recently, Pisoni and Geers (2000) reported that simple forward digit span measures of verbal working memory were significantly correlated with spoken word recognition scores even after potentially confounding variables were statistically controlled for. The present study replicates and extends these initial findings to the full set of 176 participants in the CID cochlear implant study. The pooled data indicate that despite statistical “partialling-out” of differences in chronological age, communication mode, duration of deafness, duration of device use, age at onset of deafness, number of active electrodes, and speech feature discrimination, significant correlations still remain between digit span and several measures of spoken word recognition. Strong correlations were also observed between speaking rate and both forward and backward digit span, a result that is similar to previously reported findings in normalhearing adults and children. The results suggest that perhaps as much as 20% of the currently unexplained variance in spoken word recognition scores may be independently accounted for by individual differences in cognitive factors related to the speed and efficiency with which phonological and lexical representations of spoken words are maintained in and retrieved from working memory. A smaller percentage, perhaps about 7% of the currently unexplained variance in spoken word recognition scores, may be accounted for in terms of working memory capacity. We discuss how these relationships may arise and their contribution to subsequent speech and language development in prelingually deaf children who use cochlear implants. PMID:12612485

Speculative Method in Digital Education Research

ERIC Educational Resources Information Center

Ross, Jen

2017-01-01

The question of "what works" is currently dominating educational research, often to the exclusion of other kinds of inquiries and without enough recognition of its limitations. At the same time, digital education practice, policy and research over-emphasises control, efficiency and enhancement, neglecting the "not-yetness" of…

Warped document image correction method based on heterogeneous registration strategies

NASA Astrophysics Data System (ADS)

Tong, Lijing; Zhan, Guoliang; Peng, Quanyao; Li, Yang; Li, Yifan

2013-03-01

With the popularity of digital camera and the application requirement of digitalized document images, using digital cameras to digitalize document images has become an irresistible trend. However, the warping of the document surface impacts on the quality of the Optical Character Recognition (OCR) system seriously. To improve the warped document image's vision quality and the OCR rate, this paper proposed a warped document image correction method based on heterogeneous registration strategies. This method mosaics two warped images of the same document from different viewpoints. Firstly, two feature points are selected from one image. Then the two feature points are registered in the other image base on heterogeneous registration strategies. At last, image mosaics are done for the two images, and the best mosaiced image is selected by OCR recognition results. As a result, for the best mosaiced image, the distortions are mostly removed and the OCR results are improved markedly. Experimental results show that the proposed method can resolve the issue of warped document image correction more effectively.

Incoherent optical generalized Hough transform: pattern recognition and feature extraction applications

NASA Astrophysics Data System (ADS)

Fernández, Ariel; Ferrari, José A.

2017-05-01

Pattern recognition and feature extraction are image processing applications of great interest in defect inspection and robot vision among others. In comparison to purely digital methods, the attractiveness of optical processors for pattern recognition lies in their highly parallel operation and real-time processing capability. This work presents an optical implementation of the generalized Hough transform (GHT), a well-established technique for recognition of geometrical features in binary images. Detection of a geometric feature under the GHT is accomplished by mapping the original image to an accumulator space; the large computational requirements for this mapping make the optical implementation an attractive alternative to digital-only methods. We explore an optical setup where the transformation is obtained, and the size and orientation parameters can be controlled, allowing for dynamic scale and orientation-variant pattern recognition. A compact system for the above purposes results from the use of an electrically tunable lens for scale control and a pupil mask implemented on a high-contrast spatial light modulator for orientation/shape variation of the template. Real-time can also be achieved. In addition, by thresholding of the GHT and optically inverse transforming, the previously detected features of interest can be extracted.

Karen and George: Face Recognition by Visually Impaired Children.

ERIC Educational Resources Information Center

Ellis, Hadyn D.; And Others

1988-01-01

Two visually impaired children, aged 8 and 10, appeared to have severe difficulty in recognizing faces. After assessment, it became apparent that only one had unusually poor facial recognition skills. After training, which included matching face photographs, schematic faces, and digitized faces, there was no evidence of any improvement.…

[Johann Misch Astrophilus' book "Medicina Pauperum" in Hungarian. Copy of a lost or hidden book from 1660].

PubMed

Kiss, István; Tavaszy, Mariann; Kiss, Gergely

2011-07-03

Doctors and pharmacies in the 15th Century only used handwritten copies of the prescription collections available in their time. At the beginning of book printing the publishing of prescription collections immediately became popular. They could be found on the pages of medical and pharmaceutical books of many various editions with different structure and origin, as the forerunner of the official pharmacopoeias. From the 16th Century onwards books with the title "Medicina Pauperum" were published which helped the educated people to tend to themselves, the household, the servants and their immediate surroundings case of an illness. The first work specifically on the topic or of genre of the "Medicina Pauperum" according to our knowledge appeared in Hungarian in the year 1660 and currently seems to survived only in fragments under the title of "Medicina Pauperum", from an unknown author. A rare incident occurred in the present days as a "book" believed to be lost for us turned up from thin air. It is a "copied" manuscript in the size of 97×139 mm attached to the ribs with hemp cord, cropped around and in an unbound state. The book known before only in fractions is now available entirety handwritten on 318 pages, distributed to seven distinct parts. The research of its origin suggests that the author lived and worked in Nagyszombat and was called Johann Misch Astrophilus. The identification of the printing office was possible thanks to the examination of the initials and the gaudily, as well as the fonts and the watermark. By these results the printing very likely occurred in the Brewer Printing Press in Lőcse. For the possibility of more extensive research and value preservation the manuscript was bounded. The facsimile edition contains the magnified and digitalized pages of the original one and is published in numbered issues.

Micro-Controllable, Multi-Functional Interface Module for Digital MP: A Wearable Computer Security Application

DTIC Science & Technology

2004-05-01

Army Soldier System Command: http://www.natick.armv.mil Role Name Facial Recognition Program Manager, Army Technical Lead Mark Chandler...security force with a facial recognition system. Mike Holloran, technology officer with the 6 Fleet, directed LCDR Hoa Ho and CAPT(s) Todd Morgan to...USN 6th Fleet was accomplished with the admiral expressing his support for continuing the evaluation of the a facial recognition system. This went

Development of an Autonomous Face Recognition Machine.

DTIC Science & Technology

1986-12-08

This approach, like Baron’s, would be a very time consuming task. The problem of locating a face in Bromley’s work was the least complex of the three...top level design and the development and design decisions that were made in developing the Autonomous Face Recognition Machine (AFRM). The chapter is...images within a digital image. The second sectio examines the algorithm used in performing face recognition. The decision to divide the development

Three learning phases for radial-basis-function networks.

PubMed

Schwenker, F; Kestler, H A; Palm, G

2001-05-01

In this paper, learning algorithms for radial basis function (RBF) networks are discussed. Whereas multilayer perceptrons (MLP) are typically trained with backpropagation algorithms, starting the training procedure with a random initialization of the MLP's parameters, an RBF network may be trained in many different ways. We categorize these RBF training methods into one-, two-, and three-phase learning schemes. Two-phase RBF learning is a very common learning scheme. The two layers of an RBF network are learnt separately; first the RBF layer is trained, including the adaptation of centers and scaling parameters, and then the weights of the output layer are adapted. RBF centers may be trained by clustering, vector quantization and classification tree algorithms, and the output layer by supervised learning (through gradient descent or pseudo inverse solution). Results from numerical experiments of RBF classifiers trained by two-phase learning are presented in three completely different pattern recognition applications: (a) the classification of 3D visual objects; (b) the recognition hand-written digits (2D objects); and (c) the categorization of high-resolution electrocardiograms given as a time series (ID objects) and as a set of features extracted from these time series. In these applications, it can be observed that the performance of RBF classifiers trained with two-phase learning can be improved through a third backpropagation-like training phase of the RBF network, adapting the whole set of parameters (RBF centers, scaling parameters, and output layer weights) simultaneously. This, we call three-phase learning in RBF networks. A practical advantage of two- and three-phase learning in RBF networks is the possibility to use unlabeled training data for the first training phase. Support vector (SV) learning in RBF networks is a different learning approach. SV learning can be considered, in this context of learning, as a special type of one-phase learning, where only the output layer weights of the RBF network are calculated, and the RBF centers are restricted to be a subset of the training data. Numerical experiments with several classifier schemes including k-nearest-neighbor, learning vector quantization and RBF classifiers trained through two-phase, three-phase and support vector learning are given. The performance of the RBF classifiers trained through SV learning and three-phase learning are superior to the results of two-phase learning, but SV learning often leads to complex network structures, since the number of support vectors is not a small fraction of the total number of data points.

Multimedia Classifier

NASA Astrophysics Data System (ADS)

Costache, G. N.; Gavat, I.

2004-09-01

Along with the aggressive growing of the amount of digital data available (text, audio samples, digital photos and digital movies joined all in the multimedia domain) the need for classification, recognition and retrieval of this kind of data became very important. In this paper will be presented a system structure to handle multimedia data based on a recognition perspective. The main processing steps realized for the interesting multimedia objects are: first, the parameterization, by analysis, in order to obtain a description based on features, forming the parameter vector; second, a classification, generally with a hierarchical structure to make the necessary decisions. For audio signals, both speech and music, the derived perceptual features are the melcepstral (MFCC) and the perceptual linear predictive (PLP) coefficients. For images, the derived features are the geometric parameters of the speaker mouth. The hierarchical classifier consists generally in a clustering stage, based on the Kohonnen Self-Organizing Maps (SOM) and a final stage, based on a powerful classification algorithm called Support Vector Machines (SVM). The system, in specific variants, is applied with good results in two tasks: the first, is a bimodal speech recognition which uses features obtained from speech signal fused to features obtained from speaker's image and the second is a music retrieval from large music database.

Web Surveys to Digital Movies: Technological Tools of the Trade.

ERIC Educational Resources Information Center

Fetterman, David M.

2002-01-01

Highlights some of the technological tools used by educational researchers today, focusing on data collection related tools such as Web surveys, digital photography, voice recognition and transcription, file sharing and virtual office, videoconferencing on the Internet, instantaneous chat and chat rooms, reporting and dissemination, and digital…

Evaluation of Speech Recognition of Cochlear Implant Recipients Using Adaptive, Digital Remote Microphone Technology and a Speech Enhancement Sound Processing Algorithm.

PubMed

Wolfe, Jace; Morais, Mila; Schafer, Erin; Agrawal, Smita; Koch, Dawn

2015-05-01

Cochlear implant recipients often experience difficulty with understanding speech in the presence of noise. Cochlear implant manufacturers have developed sound processing algorithms designed to improve speech recognition in noise, and research has shown these technologies to be effective. Remote microphone technology utilizing adaptive, digital wireless radio transmission has also been shown to provide significant improvement in speech recognition in noise. There are no studies examining the potential improvement in speech recognition in noise when these two technologies are used simultaneously. The goal of this study was to evaluate the potential benefits and limitations associated with the simultaneous use of a sound processing algorithm designed to improve performance in noise (Advanced Bionics ClearVoice) and a remote microphone system that incorporates adaptive, digital wireless radio transmission (Phonak Roger). A two-by-two way repeated measures design was used to examine performance differences obtained without these technologies compared to the use of each technology separately as well as the simultaneous use of both technologies. Eleven Advanced Bionics (AB) cochlear implant recipients, ages 11 to 68 yr. AzBio sentence recognition was measured in quiet and in the presence of classroom noise ranging in level from 50 to 80 dBA in 5-dB steps. Performance was evaluated in four conditions: (1) No ClearVoice and no Roger, (2) ClearVoice enabled without the use of Roger, (3) ClearVoice disabled with Roger enabled, and (4) simultaneous use of ClearVoice and Roger. Speech recognition in quiet was better than speech recognition in noise for all conditions. Use of ClearVoice and Roger each provided significant improvement in speech recognition in noise. The best performance in noise was obtained with the simultaneous use of ClearVoice and Roger. ClearVoice and Roger technology each improves speech recognition in noise, particularly when used at the same time. Because ClearVoice does not degrade performance in quiet settings, clinicians should consider recommending ClearVoice for routine, full-time use for AB implant recipients. Roger should be used in all instances in which remote microphone technology may assist the user in understanding speech in the presence of noise. American Academy of Audiology.

Effectiveness of feature and classifier algorithms in character recognition systems

NASA Astrophysics Data System (ADS)

Wilson, Charles L.

1993-04-01

At the first Census Optical Character Recognition Systems Conference, NIST generated accuracy data for more than character recognition systems. Most systems were tested on the recognition of isolated digits and upper and lower case alphabetic characters. The recognition experiments were performed on sample sizes of 58,000 digits, and 12,000 upper and lower case alphabetic characters. The algorithms used by the 26 conference participants included rule-based methods, image-based methods, statistical methods, and neural networks. The neural network methods included Multi-Layer Perceptron's, Learned Vector Quantitization, Neocognitrons, and cascaded neural networks. In this paper 11 different systems are compared using correlations between the answers of different systems, comparing the decrease in error rate as a function of confidence of recognition, and comparing the writer dependence of recognition. This comparison shows that methods that used different algorithms for feature extraction and recognition performed with very high levels of correlation. This is true for neural network systems, hybrid systems, and statistically based systems, and leads to the conclusion that neural networks have not yet demonstrated a clear superiority to more conventional statistical methods. Comparison of these results with the models of Vapnick (for estimation problems), MacKay (for Bayesian statistical models), Moody (for effective parameterization), and Boltzmann models (for information content) demonstrate that as the limits of training data variance are approached, all classifier systems have similar statistical properties. The limiting condition can only be approached for sufficiently rich feature sets because the accuracy limit is controlled by the available information content of the training set, which must pass through the feature extraction process prior to classification.

Prediction of Word Recognition in the First Half of Grade 1

ERIC Educational Resources Information Center

Snel, M. J.; Aarnoutse, C. A. J.; Terwel, J.; van Leeuwe, J. F. J.; van der Veld, W. M.

2016-01-01

Early detection of reading problems is important to prevent an enduring lag in reading skills. We studied the relationship between speed of word recognition (after six months of grade 1 education) and four kindergarten pre-literacy skills: letter knowledge, phonological awareness and naming speed for both digits and letters. Our sample consisted…

Chemical recognition of gases and gas mixtures with terahertz waves.

PubMed

Jacobsen, R H; Mittleman, D M; Nuss, M C

1996-12-15

A time-domain chemical-recognition system for classifying gases and analyzing gas mixtures is presented. We analyze the free induction decay exhibited by gases excited by far-infrared (terahertz) pulses in the time domain, using digital signal-processing techniques. A simple geometric picture is used for the classif ication of the waveforms measured for unknown gas species. We demonstrate how the recognition system can be used to determine the partial pressures of an ammonia-water gas mixture.

Chemical recognition of gases and gas mixtures with terahertz waves

NASA Astrophysics Data System (ADS)

Jacobsen, R. H.; Mittleman, D. M.; Nuss, M. C.

1996-12-01

A time-domain chemical-recognition system for classifying gases and analyzing gas mixtures is presented. We analyze the free induction decay exhibited by gases excited by far-infrared (terahertz) pulses in the time domain, using digital signal-processing techniques. A simple geometric picture is used for the classification of the waveforms measured for unknown gas species. We demonstrate how the recognition system can be used to determine the partial pressures of an ammonia-water gas mixture.

Facial Recognition in a Discus Fish (Cichlidae): Experimental Approach Using Digital Models

PubMed Central

Satoh, Shun; Tanaka, Hirokazu; Kohda, Masanori

2016-01-01

A number of mammals and birds are known to be capable of visually discriminating between familiar and unfamiliar individuals, depending on facial patterns in some species. Many fish also visually recognize other conspecifics individually, and previous studies report that facial color patterns can be an initial signal for individual recognition. For example, a cichlid fish and a damselfish will use individual-specific color patterns that develop only in the facial area. However, it remains to be determined whether the facial area is an especially favorable site for visual signals in fish, and if so why? The monogamous discus fish, Symphysopdon aequifasciatus (Cichlidae), is capable of visually distinguishing its pair-partner from other conspecifics. Discus fish have individual-specific coloration patterns on entire body including the facial area, frontal head, trunk and vertical fins. If the facial area is an inherently important site for the visual cues, this species will use facial patterns for individual recognition, but otherwise they will use patterns on other body parts as well. We used modified digital models to examine whether discus fish use only facial coloration for individual recognition. Digital models of four different combinations of familiar and unfamiliar fish faces and bodies were displayed in frontal and lateral views. Focal fish frequently performed partner-specific displays towards partner-face models, and did aggressive displays towards models of non-partner’s faces. We conclude that to identify individuals this fish does not depend on frontal color patterns but does on lateral facial color patterns, although they have unique color patterns on the other parts of body. We discuss the significance of facial coloration for individual recognition in fish compared with birds and mammals. PMID:27191162

Facial Recognition in a Discus Fish (Cichlidae): Experimental Approach Using Digital Models.

PubMed

Satoh, Shun; Tanaka, Hirokazu; Kohda, Masanori

2016-01-01

A number of mammals and birds are known to be capable of visually discriminating between familiar and unfamiliar individuals, depending on facial patterns in some species. Many fish also visually recognize other conspecifics individually, and previous studies report that facial color patterns can be an initial signal for individual recognition. For example, a cichlid fish and a damselfish will use individual-specific color patterns that develop only in the facial area. However, it remains to be determined whether the facial area is an especially favorable site for visual signals in fish, and if so why? The monogamous discus fish, Symphysopdon aequifasciatus (Cichlidae), is capable of visually distinguishing its pair-partner from other conspecifics. Discus fish have individual-specific coloration patterns on entire body including the facial area, frontal head, trunk and vertical fins. If the facial area is an inherently important site for the visual cues, this species will use facial patterns for individual recognition, but otherwise they will use patterns on other body parts as well. We used modified digital models to examine whether discus fish use only facial coloration for individual recognition. Digital models of four different combinations of familiar and unfamiliar fish faces and bodies were displayed in frontal and lateral views. Focal fish frequently performed partner-specific displays towards partner-face models, and did aggressive displays towards models of non-partner's faces. We conclude that to identify individuals this fish does not depend on frontal color patterns but does on lateral facial color patterns, although they have unique color patterns on the other parts of body. We discuss the significance of facial coloration for individual recognition in fish compared with birds and mammals.

The use of discrete-event simulation modeling to compare handwritten and electronic prescribing systems.

PubMed

Ghany, Ahmad; Vassanji, Karim; Kuziemsky, Craig; Keshavjee, Karim

2013-01-01

Electronic prescribing (e-prescribing) is expected to bring many benefits to Canadian healthcare, such as a reduction in errors and adverse drug reactions. As there currently is no functioning e-prescribing system in Canada that is completely electronic, we are unable to evaluate the performance of a live system. An alternative approach is to use simulation modeling for evaluation. We developed two discrete-event simulation models, one of the current handwritten prescribing system and one of a proposed e-prescribing system, to compare the performance of these two systems. We were able to compare the number of processes in each model, workflow efficiency, and the distribution of patients or prescriptions. Although we were able to compare these models to each other, using discrete-event simulation software was challenging. We were limited in the number of variables we could measure. We discovered non-linear processes and feedback loops in both models that could not be adequately represented using discrete-event simulation software. Finally, interactions between entities in both models could not be modeled using this type of software. We have come to the conclusion that a more appropriate approach to modeling both the handwritten and electronic prescribing systems would be to use a complex adaptive systems approach using agent-based modeling or systems-based modeling.

Degraded character recognition based on gradient pattern

NASA Astrophysics Data System (ADS)

Babu, D. R. Ramesh; Ravishankar, M.; Kumar, Manish; Wadera, Kevin; Raj, Aakash

2010-02-01

Degraded character recognition is a challenging problem in the field of Optical Character Recognition (OCR). The performance of an optical character recognition depends upon printed quality of the input documents. Many OCRs have been designed which correctly identifies the fine printed documents. But, very few reported work has been found on the recognition of the degraded documents. The efficiency of the OCRs system decreases if the input image is degraded. In this paper, a novel approach based on gradient pattern for recognizing degraded printed character is proposed. The approach makes use of gradient pattern of an individual character for recognition. Experiments were conducted on character image that is either digitally written or a degraded character extracted from historical documents and the results are found to be satisfactory.

Object recognition of ladar with support vector machine

NASA Astrophysics Data System (ADS)

Sun, Jian-Feng; Li, Qi; Wang, Qi

2005-01-01

Intensity, range and Doppler images can be obtained by using laser radar. Laser radar can detect much more object information than other detecting sensor, such as passive infrared imaging and synthetic aperture radar (SAR), so it is well suited as the sensor of object recognition. Traditional method of laser radar object recognition is extracting target features, which can be influenced by noise. In this paper, a laser radar recognition method-Support Vector Machine is introduced. Support Vector Machine (SVM) is a new hotspot of recognition research after neural network. It has well performance on digital written and face recognition. Two series experiments about SVM designed for preprocessing and non-preprocessing samples are performed by real laser radar images, and the experiments results are compared.

Fuzzy Logic-Based Audio Pattern Recognition

NASA Astrophysics Data System (ADS)

Malcangi, M.

2008-11-01

Audio and audio-pattern recognition is becoming one of the most important technologies to automatically control embedded systems. Fuzzy logic may be the most important enabling methodology due to its ability to rapidly and economically model such application. An audio and audio-pattern recognition engine based on fuzzy logic has been developed for use in very low-cost and deeply embedded systems to automate human-to-machine and machine-to-machine interaction. This engine consists of simple digital signal-processing algorithms for feature extraction and normalization, and a set of pattern-recognition rules manually tuned or automatically tuned by a self-learning process.

Using Digital Badges for Developing High School Chemistry Laboratory Skills

ERIC Educational Resources Information Center

Hennah, Naomi; Seery, Michael K.

2017-01-01

Digital badges are emerging as an approach to offer microaccreditation for student achievements obtained in ongoing course work. They act to offer a formal recognition and framework for multiple small components which together make a significant contribution to student learning. Badges are promoted as a way of highlighting these particular…

Archive of digital boomer seismic reflection data collected during USGS cruises 94GFP01, 95GFP01, 96GFP01, 97GFP01, and 98GFP02 in Lakes Pontchartrain, Borgne, and Maurepas, Louisiana, 1994-1998

USGS Publications Warehouse

Calderon, Karynna; Dadisman, Shawn V.; Kindinger, Jack G.; Williams, S. Jeffress; Flocks, James G.; Penland, Shea; Wiese, Dana S.

2003-01-01

The U.S. Geological Survey, in cooperation with the University of New Orleans, the Lake Pontchartrain Basin Foundation, the National Oceanic and Atmospheric Administration, the Coalition to Restore Coastal Louisiana, the U.S. Army Corps of Engineers, the Environmental Protection Agency, and the University of Georgia, conducted five geophysical surveys of Lakes Pontchartrain, Borgne, and Maurepas in Louisiana from 1994 to 1998. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, observers' logbooks, GIS information, and formal FGDC metadata. In addition, a filtered and gained digital GIF image of each seismic profile is provided. Refer to the Acronyms page for expansion of acronyms and abbreviations used in this report. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Examples of SU processing scripts and in-house (USGS) software for viewing SEG-Y headers (Zihlman, 1992) are also provided. Processed profile images, trackline maps, navigation files, and formal metadata may be viewed with a web browser, and scanned handwritten logbooks may be viewed with Adobe Reader. To access the information contained on these discs, open the file 'index.htm' located at the top level of the discs using a web browser. This report also contains hyperlinks to USGS collaborators and other agencies. These links are only accessible if access to the Internet is available while viewing these documents.

Rocket Ozone Data Recovery for Digital Archival

NASA Astrophysics Data System (ADS)

Hwang, S. H.; Krueger, A. J.; Hilsenrath, E.; Haffner, D. P.; Bhartia, P. K.

2014-12-01

Ozone distributions in the photochemically-controlled upper stratosphere and mesosphere were first measured using spectrometers on V-2 rockets after WWII. The IGY(1957-1958) spurred development of new optical and chemical instruments for flight on meteorological and sounding rockets. In the early 1960's, the US Navy developed an Arcas rocket-borne optical ozonesonde and NASA GSFC developed chemiluminescent ozonesonde onboard Nike_Cajun and Arcas rocket. The Navy optical ozone program was moved in 1969 to GSFC where rocket ozone research was expanded and continued until 1994 using Super Loki-Dart rocket at 11 sites in the range of 0-65N and 35W-160W. Over 300 optical ozone soundings and 40 chemiluminescent soundings were made. The data have been used to produce the US Standard Ozone Atmosphere, determine seasonal and diurnal variations, and validate early photochemical models. The current effort includes soundings conducted by Australia, Japan, and Korea using optical techniques. New satellite ozone sounding techniques were initially calibrated and later validated using the rocket ozone data. As satellite techniques superseded the rocket methods, the sponsoring agencies lost interest in the data and many of those records have been discarded. The current task intends to recover as much of the data as possible from the private records of the experimenters and their publications, and to archive those records in the WOUDC (World Ozone and Ultraviolet Data Centre). The original data records are handwritten tabulations, computer printouts that are scanned with OCR techniques, and plots digitized from publications. This newly recovered digital rocket ozone profile data from 1965 to 2002 could make significant contributions to the Earth science community in atmospheric research including long-term trend analysis.

[Computer graphic display of retinal examination results. Software improving the quality of documenting fundus changes].

PubMed

Jürgens, Clemens; Grossjohann, Rico; Czepita, Damian; Tost, Frank

2009-01-01

Graphic documentation of retinal examination results in clinical ophthalmological practice is often depicted using pictures or in handwritten form. Popular software products used to describe changes in the fundus do not vary much from simple graphic programs that enable to insert, scale and edit basic graphic elements such as: a circle, rectangle, arrow or text. Displaying the results of retinal examinations in a unified way is difficult to achieve. Therefore, we devised and implemented modern software tools for this purpose. A computer program enabling to quickly and intuitively form graphs of the fundus, that can be digitally archived or printed was created. Especially for the needs of ophthalmological clinics, a set of standard digital symbols used to document the results of retinal examinations was developed and installed in a library of graphic symbols. These symbols are divided into the following categories: preoperative, postoperative, neovascularization, retinopathy of prematurity. The appropriate symbol can be selected with a click of the mouse and dragged-and-dropped on the canvas of the fundus. Current forms of documenting results of retinal examinations are unsatisfactory, due to the fact that they are time consuming and imprecise. Unequivocal interpretation is difficult or in some cases impossible. Using the developed computer program a sketch of the fundus can be created much more quickly than by hand drawing. Additionally the quality of the medica documentation using a system of well described and standardized symbols will be enhanced. (1) Graphic symbols used to document the results of retinal examinations are a part of everyday clinical practice. (2) The designed computer program will allow quick and intuitive graphical creation of fundus sketches that can be either digitally archived or printed.

Maintenance rehearsal: the key to the role attention plays in storage and forgetting.

PubMed

McFarlane, Kimberley A; Humphreys, Michael S

2012-07-01

Research with the maintenance-rehearsal paradigm, in which word pairs are rehearsed as distractor material during a series of digit recall trials, has previously indicated that low frequency and new word pairs capture attention to a greater degree than high frequency and old word pairs. This impacts delayed recognition of the pairs and interferes with immediate digit recall. The effect on immediate digit recall may provide the missing converging evidence for the role of attention in memory. In the current study, 3 experiments were conducted to further investigate the role of attention capture and novelty in storage and forgetting. In addition to the previously established effects, the novelty of switching rehearsal between 2 pairs was found to impair both digit recall and memory for the first pair. The attentional effects we obtained were dependent upon participant expectation, and forgetting appears to be due to interference with consolidation rather than decay or traditional associative interference. Finally, the attentional effects we observed in associative recognition were primarily reflected in a lowering of the false alarm rate with increases in the strength of the parent pairs. Although dual-process models can accommodate this finding on the assumption that recollection is invoked at test alongside familiarity, we showed that the level of recall in this paradigm is so small that recollection can be ruled out. Accordingly, our results are challenging for the existing models of associative recognition to accommodate. 2012 APA, all rights reserved

The Design of a Single-Bit CMOS Image Sensor for Iris Recognition Applications

PubMed Central

Park, Keunyeol; Song, Minkyu

2018-01-01

This paper presents a single-bit CMOS image sensor (CIS) that uses a data processing technique with an edge detection block for simple iris segmentation. In order to recognize the iris image, the image sensor conventionally captures high-resolution image data in digital code, extracts the iris data, and then compares it with a reference image through a recognition algorithm. However, in this case, the frame rate decreases by the time required for digital signal conversion of multi-bit digital data through the analog-to-digital converter (ADC) in the CIS. In order to reduce the overall processing time as well as the power consumption, we propose a data processing technique with an exclusive OR (XOR) logic gate to obtain single-bit and edge detection image data instead of multi-bit image data through the ADC. In addition, we propose a logarithmic counter to efficiently measure single-bit image data that can be applied to the iris recognition algorithm. The effective area of the proposed single-bit image sensor (174 × 144 pixel) is 2.84 mm2 with a 0.18 μm 1-poly 4-metal CMOS image sensor process. The power consumption of the proposed single-bit CIS is 2.8 mW with a 3.3 V of supply voltage and 520 frame/s of the maximum frame rates. The error rate of the ADC is 0.24 least significant bit (LSB) on an 8-bit ADC basis at a 50 MHz sampling frequency. PMID:29495273

The Design of a Single-Bit CMOS Image Sensor for Iris Recognition Applications.

PubMed

Park, Keunyeol; Song, Minkyu; Kim, Soo Youn

2018-02-24

This paper presents a single-bit CMOS image sensor (CIS) that uses a data processing technique with an edge detection block for simple iris segmentation. In order to recognize the iris image, the image sensor conventionally captures high-resolution image data in digital code, extracts the iris data, and then compares it with a reference image through a recognition algorithm. However, in this case, the frame rate decreases by the time required for digital signal conversion of multi-bit digital data through the analog-to-digital converter (ADC) in the CIS. In order to reduce the overall processing time as well as the power consumption, we propose a data processing technique with an exclusive OR (XOR) logic gate to obtain single-bit and edge detection image data instead of multi-bit image data through the ADC. In addition, we propose a logarithmic counter to efficiently measure single-bit image data that can be applied to the iris recognition algorithm. The effective area of the proposed single-bit image sensor (174 × 144 pixel) is 2.84 mm² with a 0.18 μm 1-poly 4-metal CMOS image sensor process. The power consumption of the proposed single-bit CIS is 2.8 mW with a 3.3 V of supply voltage and 520 frame/s of the maximum frame rates. The error rate of the ADC is 0.24 least significant bit (LSB) on an 8-bit ADC basis at a 50 MHz sampling frequency.

Computer-implemented land use classification with pattern recognition software and ERTS digital data. [Mississippi coastal plains

NASA Technical Reports Server (NTRS)

Joyce, A. T.

1974-01-01

Significant progress has been made in the classification of surface conditions (land uses) with computer-implemented techniques based on the use of ERTS digital data and pattern recognition software. The supervised technique presently used at the NASA Earth Resources Laboratory is based on maximum likelihood ratioing with a digital table look-up approach to classification. After classification, colors are assigned to the various surface conditions (land uses) classified, and the color-coded classification is film recorded on either positive or negative 9 1/2 in. film at the scale desired. Prints of the film strips are then mosaicked and photographed to produce a land use map in the format desired. Computer extraction of statistical information is performed to show the extent of each surface condition (land use) within any given land unit that can be identified in the image. Evaluations of the product indicate that classification accuracy is well within the limits for use by land resource managers and administrators. Classifications performed with digital data acquired during different seasons indicate that the combination of two or more classifications offer even better accuracy.

Studies in automatic speech recognition and its application in aerospace

NASA Astrophysics Data System (ADS)

Taylor, Michael Robinson

Human communication is characterized in terms of the spectral and temporal dimensions of speech waveforms. Electronic speech recognition strategies based on Dynamic Time Warping and Markov Model algorithms are described and typical digit recognition error rates are tabulated. The application of Direct Voice Input (DVI) as an interface between man and machine is explored within the context of civil and military aerospace programmes. Sources of physical and emotional stress affecting speech production within military high performance aircraft are identified. Experimental results are reported which quantify fundamental frequency and coarse temporal dimensions of male speech as a function of the vibration, linear acceleration and noise levels typical of aerospace environments; preliminary indications of acoustic phonetic variability reported by other researchers are summarized. Connected whole-word pattern recognition error rates are presented for digits spoken under controlled Gz sinusoidal whole-body vibration. Correlations are made between significant increases in recognition error rate and resonance of the abdomen-thorax and head subsystems of the body. The phenomenon of vibrato style speech produced under low frequency whole-body Gz vibration is also examined. Interactive DVI system architectures and avionic data bus integration concepts are outlined together with design procedures for the efficient development of pilot-vehicle command and control protocols.

Defining event reconstruction of digital crime scenes.

PubMed

Carrier, Brian D; Spafford, Eugene H

2004-11-01

Event reconstruction plays a critical role in solving physical crimes by explaining why a piece of physical evidence has certain characteristics. With digital crimes, the current focus has been on the recognition and identification of digital evidence using an object's characteristics, but not on the identification of the events that caused the characteristics. This paper examines digital event reconstruction and proposes a process model and procedure that can be used for a digital crime scene. The model has been designed so that it can apply to physical crime scenes, can support the unique aspects of a digital crime scene, and can be implemented in software to automate part of the process. We also examine the differences between physical event reconstruction and digital event reconstruction.

Study of optical design of three-dimensional digital ophthalmoscopes.

PubMed

Fang, Yi-Chin; Yen, Chih-Ta; Chu, Chin-Hsien

2015-10-01

This study primarily involves using optical zoom structures to design a three-dimensional (3D) human-eye optical sensory system with infrared and visible light. According to experimental data on two-dimensional (2D) and 3D images, human-eye recognition of 3D images is substantially higher (approximately 13.182%) than that of 2D images. Thus, 3D images are more effective than 2D images when they are used at work or in high-recognition devices. In the optical system design, infrared and visible light wavebands were incorporated as light sources to perform simulations. The results can be used to facilitate the design of optical systems suitable for 3D digital ophthalmoscopes.

Age and gender-invariant features of handwritten signatures for verification systems

NASA Astrophysics Data System (ADS)

AbdAli, Sura; Putz-Leszczynska, Joanna

2014-11-01

Handwritten signature is one of the most natural biometrics, the study of human physiological and behavioral patterns. Behavioral biometrics includes signatures that may be different due to its owner gender or age because of intrinsic or extrinsic factors. This paper presents the results of the author's research on age and gender influence on verification factors. The experiments in this research were conducted using a database that contains signatures and their associated metadata. The used algorithm is based on the universal forgery feature idea, where the global classifier is able to classify a signature as a genuine one or, as a forgery, without the actual knowledge of the signature template and its owner. Additionally, the reduction of the dimensionality with the MRMR method is discussed.

Classification and Verification of Handwritten Signatures with Time Causal Information Theory Quantifiers.

PubMed

Rosso, Osvaldo A; Ospina, Raydonal; Frery, Alejandro C

2016-01-01

We present a new approach for handwritten signature classification and verification based on descriptors stemming from time causal information theory. The proposal uses the Shannon entropy, the statistical complexity, and the Fisher information evaluated over the Bandt and Pompe symbolization of the horizontal and vertical coordinates of signatures. These six features are easy and fast to compute, and they are the input to an One-Class Support Vector Machine classifier. The results are better than state-of-the-art online techniques that employ higher-dimensional feature spaces which often require specialized software and hardware. We assess the consistency of our proposal with respect to the size of the training sample, and we also use it to classify the signatures into meaningful groups.

Epistemic agency in an environmental sciences watershed investigation fostered by digital photography

NASA Astrophysics Data System (ADS)

Zimmerman, Heather Toomey; Weible, Jennifer L.

2018-05-01

This collective case study investigates the role of digital photography to support high school students' engagement in science inquiry practices during a three-week environmental sciences unit. The study's theoretical framework brings together research from digital photography, participation in environmental science practices, and epistemic agency. Data analysed include field notes and video transcripts from two groups of learners (n = 19) that focus on how high school students used digital photography during their participation in two distinct environmental monitoring practices: stream mapping and macroinvertebrate identification. Our study resulted in two findings related to the role of digital photography where students developed knowledge as they engaged in environmental monitoring inquiry practices. First, we found that digital photography was integral to the youths' epistemic agency (defined as their confidence that they could build knowledge related to science in their community) as they engaged in data collection, documenting environmental monitoring procedures, and sharing data in the classroom. Based this finding, an implication of our work is a refined view of the role of digital photography in environmental sciences education where the use of photography enhances epistemic agency in inquiry-based activities. Second, we found that the youths innovated a use of digital photography to foster a recognition that they were capable and competent in scientific procedures during a streamside study. Based on this finding, we offer a theoretical implication that expands the construct of epistemic agency; we posit that epistemic agency includes a subcomponent where the students purposefully formulate an external recognition as producers of scientific knowledge.

Food marketing towards children: brand logo recognition, food-related behavior and BMI among 3-13-year-olds in a south Indian town.

PubMed

Ueda, Peter; Tong, Leilei; Viedma, Cristobal; Chandy, Sujith J; Marrone, Gaetano; Simon, Anna; Stålsby Lundborg, Cecilia

2012-01-01

To assess exposure to marketing of unhealthy food products and its relation to food related behavior and BMI in children aged 3-13, from different socioeconomic backgrounds in a south Indian town. Child-parent pairs (n=306) were recruited at pediatric clinics. Exposure to food marketing was assessed by a digital logo recognition test. Children matched 18 logos of unhealthy food (high in fat/sugar/salt) featured in promotion material from the food industry to pictures of corresponding products. Children's nutritional knowledge, food preferences, purchase requests, eating behavior and socioeconomic characteristics were assessed by a digital game and parental questionnaires. Anthropometric measurements were recorded. Recognition rates for the brand logos ranged from 30% to 80%. Logo recognition ability increased with age (p<0.001) and socioeconomic level (p<0.001 comparing children in the highest and lowest of three socioeconomic groups). Adjusted for gender, age and socioeconomic group, logo recognition was associated with higher BMI (p=0.022) and nutritional knowledge (p<0.001) but not to unhealthy food preferences or purchase requests. Children from higher socioeconomic groups in the region had higher brand logo recognition ability and are possibly exposed to more food marketing. The study did not lend support to a link between exposure to marketing and poor eating behavior, distorted nutritional knowledge or increased purchase requests. The correlation between logo recognition and BMI warrants further investigation on food marketing towards children and its potential role in the increasing burden of non-communicable diseases in this part of India.

Digital Badging at The Open University: Recognition for Informal Learning

ERIC Educational Resources Information Center

Law, Patrina

2015-01-01

Awarding badges to recognise achievement is not a new development. Digital badging now offers new ways to recognise learning and motivate learners, providing evidence of skills and achievements in a variety of formal and informal settings. Badged open courses (BOCs) were piloted in various forms by the Open University (OU) in 2013 to provide a…

The Heinz Electronic Library Interactive Online System (HELIOS): Building a Digital Archive Using Imaging, OCR, and Natural Language Processing Technologies.

ERIC Educational Resources Information Center

Galloway, Edward A.; Michalek, Gabrielle V.

1995-01-01

Discusses the conversion project of the congressional papers of Senator John Heinz into digital format and the provision of electronic access to these papers by Carnegie Mellon University. Topics include collection background, project team structure, document processing, scanning, use of optical character recognition software, verification…

Goal Setting and Open Digital Badges in Higher Education

ERIC Educational Resources Information Center

Cheng, Zui; Watson, Sunnie Lee; Newby, Timothy James

2018-01-01

While Open Digital Badges (ODBs) has gained an increasing recognition as micro-credentials, many researchers foresee the role of ODBs as an innovative learning tool to enhance learning experiences beyond that of an alternative credential. However, little research has explored this topic. The purposes of this paper are to 1) argue that one way to…

Facial recognition software success rates for the identification of 3D surface reconstructed facial images: implications for patient privacy and security.

PubMed

Mazura, Jan C; Juluru, Krishna; Chen, Joseph J; Morgan, Tara A; John, Majnu; Siegel, Eliot L

2012-06-01

Image de-identification has focused on the removal of textual protected health information (PHI). Surface reconstructions of the face have the potential to reveal a subject's identity even when textual PHI is absent. This study assessed the ability of a computer application to match research subjects' 3D facial reconstructions with conventional photographs of their face. In a prospective study, 29 subjects underwent CT scans of the head and had frontal digital photographs of their face taken. Facial reconstructions of each CT dataset were generated on a 3D workstation. In phase 1, photographs of the 29 subjects undergoing CT scans were added to a digital directory and tested for recognition using facial recognition software. In phases 2-4, additional photographs were added in groups of 50 to increase the pool of possible matches and the test for recognition was repeated. As an internal control, photographs of all subjects were tested for recognition against an identical photograph. Of 3D reconstructions, 27.5% were matched correctly to corresponding photographs (95% upper CL, 40.1%). All study subject photographs were matched correctly to identical photographs (95% lower CL, 88.6%). Of 3D reconstructions, 96.6% were recognized simply as a face by the software (95% lower CL, 83.5%). Facial recognition software has the potential to recognize features on 3D CT surface reconstructions and match these with photographs, with implications for PHI.

Automatic Target Recognition Based on Cross-Plot

PubMed Central

Wong, Kelvin Kian Loong; Abbott, Derek

2011-01-01

Automatic target recognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Target recognition was implemented based on the proposed pattern recognition concept and tested rigorously for its precision and recall performance. We conclude that Cross-plotting is able to produce a digital fingerprint of a target that correlates efficiently and effectively to signatures of patterns having its identity in a target repository. PMID:21980508

Digital Paper Technologies for Topographical Applications

DTIC Science & Technology

2011-09-19

measures examine were training time for each method, time for entry offeatures, procedural errors, handwriting recognition errors, and user preference...time for entry of features, procedural errors, handwriting recognition errors, and user preference. For these metrics, temporal association was...checkbox, text restricted to a specific list of values, etc.) that provides constraints to the handwriting recognizer. When the user fills out the form

Developmental Changes in Face Recognition during Childhood: Evidence from Upright and Inverted Faces

ERIC Educational Resources Information Center

de Heering, Adelaide; Rossion, Bruno; Maurer, Daphne

2012-01-01

Adults are experts at recognizing faces but there is controversy about how this ability develops with age. We assessed 6- to 12-year-olds and adults using a digitized version of the Benton Face Recognition Test, a sensitive tool for assessing face perception abilities. Children's response times for correct responses did not decrease between ages 6…

Design and application of pulse information acquisition and analysis system with dynamic recognition in traditional Chinese medicine.

PubMed

Zhang, Jian; Niu, Xin; Yang, Xue-zhi; Zhu, Qing-wen; Li, Hai-yan; Wang, Xuan; Zhang, Zhi-guo; Sha, Hong

2014-09-01

To design the pulse information which includes the parameter of pulse-position, pulse-number, pulse-shape and pulse-force acquisition and analysis system with function of dynamic recognition, and research the digitalization and visualization of some common cardiovascular mechanism of single pulse. To use some flexible sensors to catch the radial artery pressure pulse wave and utilize the high frequency B mode ultrasound scanning technology to synchronously obtain the information of radial extension and axial movement, by the way of dynamic images, then the gathered information was analyzed and processed together with ECG. Finally, the pulse information acquisition and analysis system was established which has the features of visualization and dynamic recognition, and it was applied to serve for ten healthy adults. The new system overcome the disadvantage of one-dimensional pulse information acquisition and process method which was common used in current research area of pulse diagnosis in traditional Chinese Medicine, initiated a new way of pulse diagnosis which has the new features of dynamic recognition, two-dimensional information acquisition, multiplex signals combination and deep data mining. The newly developed system could translate the pulse signals into digital, visual and measurable motion information of vessel.

YADCLAN: yet another digitally-controlled linear artificial neuron.

PubMed

Frenger, Paul

2003-01-01

This paper updates the author's 1999 RMBS presentation on digitally controlled linear artificial neuron design. Each neuron is based on a standard operational amplifier having excitatory and inhibitory inputs, variable gain, an amplified linear analog output and an adjustable threshold comparator for digital output. This design employs a 1-wire serial network of digitally controlled potentiometers and resistors whose resistance values are set and read back under microprocessor supervision. This system embodies several unique and useful features, including: enhanced neuronal stability, dynamic reconfigurability and network extensibility. This artificial neuronal is being employed for feature extraction and pattern recognition in an advanced robotic application.

Preserving and Archiving Astronomical Photographic Plates

NASA Astrophysics Data System (ADS)

Castelaz, M. W.; Cline, J. D.

2005-05-01

Astronomical objects change with time. New observations complement past observations recorded on photographic plates. Analyses of changes provide essential routes to information about an object's formation, constitution and evolution. Preserving a century of photographic plate observations is thus of paramount importance. Plate collections are presently widely dispersed; plates may be stored in poor conditions, and are effectively inaccessible to both researchers and historians. We describe a planned project at Pisgah Astronomical Research Institute to preserve the collections of astronomical plates in the United States by gathering them into a single storage location. Collections will be sorted, cleaned, and cataloged on-line so as to provide access to researchers. Full scientific and historic use of the material then requires the observations themselves to be accessible digitally. The project's goal will be the availability of these data as a unique, fully-maintained scientific and educational resource. The new archive will support trans-disciplinary research such as the chemistry of the Earth's atmosphere, library information science, trends in local weather patterns, and impacts of urbanization on telescope use, while the hand-written observatory logs will be a valuable resource for science historians and biographers.

Remote data entry and retrieval for law enforcement

NASA Astrophysics Data System (ADS)

Kwasowsky, Bohdan R.; Capraro, Gerard T.; Berdan, Gerald B.; Capraro, Christopher T.

1997-02-01

Law enforcement personnel need to capture and retrieve quality multimedia data in `real time' while in the field. This is not done today, for the most part. Most law enforcement officers gather data on handwritten forms and retrieve data via voice communications or fax. This approach is time consuming, costly, prone to errors, and may require months before some data are entered into a usable law enforcement database. With advances in the computing and communications industries, it is now possible to communicate with anyone using a laptop computer or personal digital assistant (PDA), given a phone line, an RF modem, or cellular capability. Many law enforcement officers have access to laptop computers within their vehicles and can stay in touch with their command center and/or retrieve data from local, state, or federal databases. However, this same capability is not available once they leave the vehicle or if the officer is on a beat, motorcycle, or horseback. This paper investigates the issues and reviews the state of the art for integrating a PDA into the gathering and retrieving of multimedia data for law enforcement.

An apple a day does not always keep the doctor away....

PubMed

Dedouit, Fabrice; Tournel, Gilles; Robert, Anne Bécart; Dutrieux, Pierre; Hédouin, Valéry; Gosset, Didier

2008-11-01

The authors describe a case of suicide in the workplace. A 45-year-old man employed by a fruit and vegetable packing company was found dead in a room containing a modified atmosphere for the packaging of fruits and vegetables. The rescue team measured the carbon monoxide (CO) concentration of the ambient air with a digital CO tester and found a level higher than 600 particles per million. Analysis of an arterial blood sample taken with an airtight syringe revealed the absence of CO but high levels of carbon dioxide (CO(2)). Autopsy revealed no significant injury and police investigators found a handwritten note of intent, describing a recent personal crisis. The authors concluded that the cause of death was suicide by asphyxiation secondary to CO(2) intoxication and notably oxygen (O(2)) depletion. This manner of suicide is rare and most cases previously described in the literature were accidental intoxications. To the best of our knowledge, this is the first case of suicide by CO(2) intoxication and O(2) depletion in a room with a modified atmosphere.

Galaxy Classifications with Deep Learning

NASA Astrophysics Data System (ADS)

Lukic, Vesna; Brüggen, Marcus

2017-06-01

Machine learning techniques have proven to be increasingly useful in astronomical applications over the last few years, for example in object classification, estimating redshifts and data mining. One example of object classification is classifying galaxy morphology. This is a tedious task to do manually, especially as the datasets become larger with surveys that have a broader and deeper search-space. The Kaggle Galaxy Zoo competition presented the challenge of writing an algorithm to find the probability that a galaxy belongs in a particular class, based on SDSS optical spectroscopy data. The use of convolutional neural networks (convnets), proved to be a popular solution to the problem, as they have also produced unprecedented classification accuracies in other image databases such as the database of handwritten digits (MNIST †) and large database of images (CIFAR ‡). We experiment with the convnets that comprised the winning solution, but using broad classifications. The effect of changing the number of layers is explored, as well as using a different activation function, to help in developing an intuition of how the networks function and to see how they can be applied to radio galaxy images.

Quantum-assisted Helmholtz machines: A quantum–classical deep learning framework for industrial datasets in near-term devices

NASA Astrophysics Data System (ADS)

Benedetti, Marcello; Realpe-Gómez, John; Perdomo-Ortiz, Alejandro

2018-07-01

Machine learning has been presented as one of the key applications for near-term quantum technologies, given its high commercial value and wide range of applicability. In this work, we introduce the quantum-assisted Helmholtz machine:a hybrid quantum–classical framework with the potential of tackling high-dimensional real-world machine learning datasets on continuous variables. Instead of using quantum computers only to assist deep learning, as previous approaches have suggested, we use deep learning to extract a low-dimensional binary representation of data, suitable for processing on relatively small quantum computers. Then, the quantum hardware and deep learning architecture work together to train an unsupervised generative model. We demonstrate this concept using 1644 quantum bits of a D-Wave 2000Q quantum device to model a sub-sampled version of the MNIST handwritten digit dataset with 16 × 16 continuous valued pixels. Although we illustrate this concept on a quantum annealer, adaptations to other quantum platforms, such as ion-trap technologies or superconducting gate-model architectures, could be explored within this flexible framework.

A regularized approach for geodesic-based semisupervised multimanifold learning.

PubMed

Fan, Mingyu; Zhang, Xiaoqin; Lin, Zhouchen; Zhang, Zhongfei; Bao, Hujun

2014-05-01

Geodesic distance, as an essential measurement for data dissimilarity, has been successfully used in manifold learning. However, most geodesic distance-based manifold learning algorithms have two limitations when applied to classification: 1) class information is rarely used in computing the geodesic distances between data points on manifolds and 2) little attention has been paid to building an explicit dimension reduction mapping for extracting the discriminative information hidden in the geodesic distances. In this paper, we regard geodesic distance as a kind of kernel, which maps data from linearly inseparable space to linear separable distance space. In doing this, a new semisupervised manifold learning algorithm, namely regularized geodesic feature learning algorithm, is proposed. The method consists of three techniques: a semisupervised graph construction method, replacement of original data points with feature vectors which are built by geodesic distances, and a new semisupervised dimension reduction method for feature vectors. Experiments on the MNIST, USPS handwritten digit data sets, MIT CBCL face versus nonface data set, and an intelligent traffic data set show the effectiveness of the proposed algorithm.

How much does language proficiency by non-native listeners influence speech audiometric tests in noise?

PubMed

Warzybok, Anna; Brand, Thomas; Wagener, Kirsten C; Kollmeier, Birger

2015-01-01

The current study investigates the extent to which the linguistic complexity of three commonly employed speech recognition tests and second language proficiency influence speech recognition thresholds (SRTs) in noise in non-native listeners. SRTs were measured for non-natives and natives using three German speech recognition tests: the digit triplet test (DTT), the Oldenburg sentence test (OLSA), and the Göttingen sentence test (GÖSA). Sixty-four non-native and eight native listeners participated. Non-natives can show native-like SRTs in noise only for the linguistically easy speech material (DTT). Furthermore, the limitation of phonemic-acoustical cues in digit triplets affects speech recognition to the same extent in non-natives and natives. For more complex and less familiar speech materials, non-natives, ranging from basic to advanced proficiency in German, require on average 3-dB better signal-to-noise ratio for the OLSA and 6-dB for the GÖSA to obtain 50% speech recognition compared to native listeners. In clinical audiology, SRT measurements with a closed-set speech test (i.e. DTT for screening or OLSA test for clinical purposes) should be used with non-native listeners rather than open-set speech tests (such as the GÖSA or HINT), especially if a closed-set version in the patient's own native language is available.

Optical character recognition of camera-captured images based on phase features

NASA Astrophysics Data System (ADS)

Diaz-Escobar, Julia; Kober, Vitaly

2015-09-01

Nowadays most of digital information is obtained using mobile devices specially smartphones. In particular, it brings the opportunity for optical character recognition in camera-captured images. For this reason many recognition applications have been recently developed such as recognition of license plates, business cards, receipts and street signal; document classification, augmented reality, language translator and so on. Camera-captured images are usually affected by geometric distortions, nonuniform illumination, shadow, noise, which make difficult the recognition task with existing systems. It is well known that the Fourier phase contains a lot of important information regardless of the Fourier magnitude. So, in this work we propose a phase-based recognition system exploiting phase-congruency features for illumination/scale invariance. The performance of the proposed system is tested in terms of miss classifications and false alarms with the help of computer simulation.

Recognizing Chinese characters in digital ink from non-native language writers using hierarchical models

NASA Astrophysics Data System (ADS)

Bai, Hao; Zhang, Xi-wen

2017-06-01

While Chinese is learned as a second language, its characters are taught step by step from their strokes to components, radicals to components, and their complex relations. Chinese Characters in digital ink from non-native language writers are deformed seriously, thus the global recognition approaches are poorer. So a progressive approach from bottom to top is presented based on hierarchical models. Hierarchical information includes strokes and hierarchical components. Each Chinese character is modeled as a hierarchical tree. Strokes in one Chinese characters in digital ink are classified with Hidden Markov Models and concatenated to the stroke symbol sequence. And then the structure of components in one ink character is extracted. According to the extraction result and the stroke symbol sequence, candidate characters are traversed and scored. Finally, the recognition candidate results are listed by descending. The method of this paper is validated by testing 19815 copies of the handwriting Chinese characters written by foreign students.

FPGA design of correlation-based pattern recognition

NASA Astrophysics Data System (ADS)

Jridi, Maher; Alfalou, Ayman

2017-05-01

Optical/Digital pattern recognition and tracking based on optical/digital correlation are a well-known techniques to detect, identify and localize a target object in a scene. Despite the limited number of treatments required by the correlation scheme, computational time and resources are relatively high. The most computational intensive treatment required by the correlation is the transformation from spatial to spectral domain and then from spectral to spatial domain. Furthermore, these transformations are used on optical/digital encryption schemes like the double random phase encryption (DRPE). In this paper, we present a VLSI architecture for the correlation scheme based on the fast Fourier transform (FFT). One interesting feature of the proposed scheme is its ability to stream image processing in order to perform correlation for video sequences. A trade-off between the hardware consumption and the robustness of the correlation can be made in order to understand the limitations of the correlation implementation in reconfigurable and portable platforms. Experimental results obtained from HDL simulations and FPGA prototype have demonstrated the advantages of the proposed scheme.

Classification and Verification of Handwritten Signatures with Time Causal Information Theory Quantifiers

PubMed Central

Ospina, Raydonal; Frery, Alejandro C.

2016-01-01

We present a new approach for handwritten signature classification and verification based on descriptors stemming from time causal information theory. The proposal uses the Shannon entropy, the statistical complexity, and the Fisher information evaluated over the Bandt and Pompe symbolization of the horizontal and vertical coordinates of signatures. These six features are easy and fast to compute, and they are the input to an One-Class Support Vector Machine classifier. The results are better than state-of-the-art online techniques that employ higher-dimensional feature spaces which often require specialized software and hardware. We assess the consistency of our proposal with respect to the size of the training sample, and we also use it to classify the signatures into meaningful groups. PMID:27907014

Gout in Duke Federico of Montefeltro (1422-1482): a new pearl of the Italian Renaissance.

PubMed

Fornaciari, Antonio; Giuffra, Valentina; Armocida, Emanuele; Caramella, Davide; Rühli, Frank J; Galassi, Francesco Maria

2018-01-01

The article examines the truthfulness of historical accounts claiming that Renaissance Duke Federico of Montefeltro (1422-1482) suffered from gout. By direct paleopathological assessment of the skeletal remains and by the philological investigation of historical and documental sources, primarily a 1461 handwritten letter by the Duke himself to his personal physician, a description of the symptoms and Renaissance therapy is offered and a final diagnosis of gout is formulated. The Duke's handwritten letter offers a rare testimony of ancient clinical self-diagnostics and Renaissance living-experience of gout. Moreover, the article also shows how an alliance between historical, documental and paleopathological methods can greatly increase the precision of retrospective diagnoses, thus helping to shed clearer light onto the antiquity and evolution of diseases.

Digitization and the Creation of Virtual Libraries: The Princeton University Image Card Catalog--Reaping the Benefits of Imaging.

ERIC Educational Resources Information Center

Henthorne, Eileen

1995-01-01

Describes a project at the Princeton University libraries that converted the pre-1981 public card catalog, using digital imaging and optical character recognition technology, to fully tagged and indexed records of text in MARC format that are available on an online database and will be added to the online catalog. (LRW)

Design of a Digital Library for Human Movement.

ERIC Educational Resources Information Center

Ben-Arie, Jezekiel; Pandit, Purvin; Rajaram, ShyamSundar

This paper is focused on a central aspect in the design of a planned digital library for human movement, i.e. on the aspect of representation and recognition of human activity from video data. The method of representation is important since it has a major impact on the design of all the other building blocks of the system such as the user…

Multivariate interactive digital analysis system /MIDAS/ - A new fast multispectral recognition system

NASA Technical Reports Server (NTRS)

Kriegler, F.; Marshall, R.; Lampert, S.; Gordon, M.; Cornell, C.; Kistler, R.

1973-01-01

The MIDAS system is a prototype, multiple-pipeline digital processor mechanizing the multivariate-Gaussian, maximum-likelihood decision algorithm operating at 200,000 pixels/second. It incorporates displays and film printer equipment under control of a general purpose midi-computer and possesses sufficient flexibility that operational versions of the equipment may be subsequently specified as subsets of the system.

Loose, Falling Characters and Sentences: The Persistence of the OCR Problem in Digital Repository E-Books

ERIC Educational Resources Information Center

Kichuk, Diana

2015-01-01

The electronic conversion of scanned image files to readable text using optical character recognition (OCR) software and the subsequent migration of raw OCR text to e-book text file formats are key remediation or media conversion technologies used in digital repository e-book production. Despite real progress, the OCR problem of reliability and…

Digital Holographic Logic

NASA Technical Reports Server (NTRS)

Preston, K., Jr.

1972-01-01

The characteristics of the holographic logic computer are discussed. The holographic operation is reviewed from the Fourier transform viewpoint, and the formation of holograms for use in performing digital logic are described. The operation of the computer with an experiment in which the binary identity function is calculated is discussed along with devices for achieving real-time performance. An application in pattern recognition using neighborhood logic is presented.

Food Marketing towards Children: Brand Logo Recognition, Food-Related Behavior and BMI among 3–13-Year-Olds in a South Indian Town

PubMed Central

Ueda, Peter; Tong, Leilei; Viedma, Cristobal; Chandy, Sujith J.; Marrone, Gaetano; Simon, Anna; Stålsby Lundborg, Cecilia

2012-01-01

Objectives To assess exposure to marketing of unhealthy food products and its relation to food related behavior and BMI in children aged 3–13, from different socioeconomic backgrounds in a south Indian town. Methods Child-parent pairs (n = 306) were recruited at pediatric clinics. Exposure to food marketing was assessed by a digital logo recognition test. Children matched 18 logos of unhealthy food (high in fat/sugar/salt) featured in promotion material from the food industry to pictures of corresponding products. Children's nutritional knowledge, food preferences, purchase requests, eating behavior and socioeconomic characteristics were assessed by a digital game and parental questionnaires. Anthropometric measurements were recorded. Results Recognition rates for the brand logos ranged from 30% to 80%. Logo recognition ability increased with age (p<0.001) and socioeconomic level (p<0.001 comparing children in the highest and lowest of three socioeconomic groups). Adjusted for gender, age and socioeconomic group, logo recognition was associated with higher BMI (p = 0.022) and nutritional knowledge (p<0.001) but not to unhealthy food preferences or purchase requests. Conclusions Children from higher socioeconomic groups in the region had higher brand logo recognition ability and are possibly exposed to more food marketing. The study did not lend support to a link between exposure to marketing and poor eating behavior, distorted nutritional knowledge or increased purchase requests. The correlation between logo recognition and BMI warrants further investigation on food marketing towards children and its potential role in the increasing burden of non-communicable diseases in this part of India. PMID:23082137

The application of automatic recognition techniques in the Apollo 9 SO-65 experiment

NASA Technical Reports Server (NTRS)

Macdonald, R. B.

1970-01-01

A synoptic feature analysis is reported on Apollo 9 remote earth surface photographs that uses the methods of statistical pattern recognition to classify density points and clusterings in digital conversion of optical data. A computer derived geological map of a geological test site indicates that geological features of the range are separable, but that specific rock types are not identifiable.

Evaluating a voice recognition system: finding the right product for your department.

PubMed

Freeh, M; Dewey, M; Brigham, L

2001-06-01

The Department of Radiology at the University of Utah Health Sciences Center has been in the process of transitioning from the traditional film-based department to a digital imaging department for the past 2 years. The department is now transitioning from the traditional method of dictating reports (dictation by radiologist to transcription to review and signing by radiologist) to a voice recognition system. The transition to digital operations will not be complete until we have the ability to directly interface the dictation process with the image review process. Voice recognition technology has advanced to the level where it can and should be an integral part of the new way of working in radiology and is an integral part of an efficient digital imaging department. The transition to voice recognition requires the task of identifying the product and the company that will best meet a department's needs. This report introduces the methods we used to evaluate the vendors and the products available as we made our purchasing decision. We discuss our evaluation method and provide a checklist that can be used by other departments to assist with their evaluation process. The criteria used in the evaluation process fall into the following major categories: user operations, technical infrastructure, medical dictionary, system interfaces, service support, cost, and company strength. Conclusions drawn from our evaluation process will be detailed, with the intention being to shorten the process for others as they embark on a similar venture. As more and more organizations investigate the many products and services that are now being offered to enhance the operations of a radiology department, it becomes increasingly important that solid methods are used to most effectively evaluate the new products. This report should help others complete the task of evaluating a voice recognition system and may be adaptable to other products as well.

Digital mammography, cancer screening: Factors important for image compression

NASA Technical Reports Server (NTRS)

Clarke, Laurence P.; Blaine, G. James; Doi, Kunio; Yaffe, Martin J.; Shtern, Faina; Brown, G. Stephen; Winfield, Daniel L.; Kallergi, Maria

1993-01-01

The use of digital mammography for breast cancer screening poses several novel problems such as development of digital sensors, computer assisted diagnosis (CAD) methods for image noise suppression, enhancement, and pattern recognition, compression algorithms for image storage, transmission, and remote diagnosis. X-ray digital mammography using novel direct digital detection schemes or film digitizers results in large data sets and, therefore, image compression methods will play a significant role in the image processing and analysis by CAD techniques. In view of the extensive compression required, the relative merit of 'virtually lossless' versus lossy methods should be determined. A brief overview is presented here of the developments of digital sensors, CAD, and compression methods currently proposed and tested for mammography. The objective of the NCI/NASA Working Group on Digital Mammography is to stimulate the interest of the image processing and compression scientific community for this medical application and identify possible dual use technologies within the NASA centers.

Working Memory Load Affects Processing Time in Spoken Word Recognition: Evidence from Eye-Movements

PubMed Central

Hadar, Britt; Skrzypek, Joshua E.; Wingfield, Arthur; Ben-David, Boaz M.

2016-01-01

In daily life, speech perception is usually accompanied by other tasks that tap into working memory capacity. However, the role of working memory on speech processing is not clear. The goal of this study was to examine how working memory load affects the timeline for spoken word recognition in ideal listening conditions. We used the “visual world” eye-tracking paradigm. The task consisted of spoken instructions referring to one of four objects depicted on a computer monitor (e.g., “point at the candle”). Half of the trials presented a phonological competitor to the target word that either overlapped in the initial syllable (onset) or at the last syllable (offset). Eye movements captured listeners' ability to differentiate the target noun from its depicted phonological competitor (e.g., candy or sandal). We manipulated working memory load by using a digit pre-load task, where participants had to retain either one (low-load) or four (high-load) spoken digits for the duration of a spoken word recognition trial. The data show that the high-load condition delayed real-time target discrimination. Specifically, a four-digit load was sufficient to delay the point of discrimination between the spoken target word and its phonological competitor. Our results emphasize the important role working memory plays in speech perception, even when performed by young adults in ideal listening conditions. PMID:27242424

An Approach to a Comprehensive Test Framework for Analysis and Evaluation of Text Line Segmentation Algorithms

PubMed Central

Brodic, Darko; Milivojevic, Dragan R.; Milivojevic, Zoran N.

2011-01-01

The paper introduces a testing framework for the evaluation and validation of text line segmentation algorithms. Text line segmentation represents the key action for correct optical character recognition. Many of the tests for the evaluation of text line segmentation algorithms deal with text databases as reference templates. Because of the mismatch, the reliable testing framework is required. Hence, a new approach to a comprehensive experimental framework for the evaluation of text line segmentation algorithms is proposed. It consists of synthetic multi-like text samples and real handwritten text as well. Although the tests are mutually independent, the results are cross-linked. The proposed method can be used for different types of scripts and languages. Furthermore, two different procedures for the evaluation of algorithm efficiency based on the obtained error type classification are proposed. The first is based on the segmentation line error description, while the second one incorporates well-known signal detection theory. Each of them has different capabilities and convenience, but they can be used as supplements to make the evaluation process efficient. Overall the proposed procedure based on the segmentation line error description has some advantages, characterized by five measures that describe measurement procedures. PMID:22164106

An approach to a comprehensive test framework for analysis and evaluation of text line segmentation algorithms.

PubMed

Brodic, Darko; Milivojevic, Dragan R; Milivojevic, Zoran N

2011-01-01

The paper introduces a testing framework for the evaluation and validation of text line segmentation algorithms. Text line segmentation represents the key action for correct optical character recognition. Many of the tests for the evaluation of text line segmentation algorithms deal with text databases as reference templates. Because of the mismatch, the reliable testing framework is required. Hence, a new approach to a comprehensive experimental framework for the evaluation of text line segmentation algorithms is proposed. It consists of synthetic multi-like text samples and real handwritten text as well. Although the tests are mutually independent, the results are cross-linked. The proposed method can be used for different types of scripts and languages. Furthermore, two different procedures for the evaluation of algorithm efficiency based on the obtained error type classification are proposed. The first is based on the segmentation line error description, while the second one incorporates well-known signal detection theory. Each of them has different capabilities and convenience, but they can be used as supplements to make the evaluation process efficient. Overall the proposed procedure based on the segmentation line error description has some advantages, characterized by five measures that describe measurement procedures.

Adaptive detection of missed text areas in OCR outputs: application to the automatic assessment of OCR quality in mass digitization projects

NASA Astrophysics Data System (ADS)

Ben Salah, Ahmed; Ragot, Nicolas; Paquet, Thierry

2013-01-01

The French National Library (BnF*) has launched many mass digitization projects in order to give access to its collection. The indexation of digital documents on Gallica (digital library of the BnF) is done through their textual content obtained thanks to service providers that use Optical Character Recognition softwares (OCR). OCR softwares have become increasingly complex systems composed of several subsystems dedicated to the analysis and the recognition of the elements in a page. However, the reliability of these systems is always an issue at stake. Indeed, in some cases, we can find errors in OCR outputs that occur because of an accumulation of several errors at different levels in the OCR process. One of the frequent errors in OCR outputs is the missed text components. The presence of such errors may lead to severe defects in digital libraries. In this paper, we investigate the detection of missed text components to control the OCR results from the collections of the French National Library. Our verification approach uses local information inside the pages based on Radon transform descriptors and Local Binary Patterns descriptors (LBP) coupled with OCR results to control their consistency. The experimental results show that our method detects 84.15% of the missed textual components, by comparing the OCR ALTO files outputs (produced by the service providers) to the images of the document.

Development of children's identity and position processing for letter, digit, and symbol strings: A cross-sectional study of the primary school years.

PubMed

Schubert, Teresa; Badcock, Nicholas; Kohnen, Saskia

2017-10-01

Letter recognition and digit recognition are critical skills for literate adults, yet few studies have considered the development of these skills in children. We conducted a nine-alternative forced-choice (9AFC) partial report task with strings of letters and digits, with typographical symbols (e.g., $, @) as a control, to investigate the development of identity and position processing in children. This task allows for the delineation of identity processing (as overall accuracy) and position coding (as the proportion of position errors). Our participants were students in Grade 1 to Grade 6, allowing us to track the development of these abilities across the primary school years. Our data suggest that although digit processing and letter processing end up with many similarities in adult readers, the developmental trajectories for identity and position processing for the two character types differ. Symbol processing showed little developmental change in terms of identity or position accuracy. We discuss the implications of our results for theories of identity and position coding: modified receptive field, multiple-route model, and lexical tuning. Despite moderate success for some theories, considerable theoretical work is required to explain the developmental trajectories of letter processing and digit processing, which might not be as closely tied in child readers as they are in adult readers. Copyright © 2017 Elsevier Inc. All rights reserved.

Design and development of an ancient Chinese document recognition system

NASA Astrophysics Data System (ADS)

Peng, Liangrui; Xiu, Pingping; Ding, Xiaoqing

2003-12-01

The digitization of ancient Chinese documents presents new challenges to OCR (Optical Character Recognition) research field due to the large character set of ancient Chinese characters, variant font types, and versatile document layout styles, as these documents are historical reflections to the thousands of years of Chinese civilization. After analyzing the general characteristics of ancient Chinese documents, we present a solution for recognition of ancient Chinese documents with regular font-types and layout-styles. Based on the previous work on multilingual OCR in TH-OCR system, we focus on the design and development of two key technologies which include character recognition and page segmentation. Experimental results show that the developed character recognition kernel of 19,635 Chinese characters outperforms our original traditional Chinese recognition kernel; Benchmarked test on printed ancient Chinese books proves that the proposed system is effective for regular ancient Chinese documents.

Human-machine interactions

DOEpatents

Forsythe, J Chris [Sandia Park, NM; Xavier, Patrick G [Albuquerque, NM; Abbott, Robert G [Albuquerque, NM; Brannon, Nathan G [Albuquerque, NM; Bernard, Michael L [Tijeras, NM; Speed, Ann E [Albuquerque, NM

2009-04-28

Digital technology utilizing a cognitive model based on human naturalistic decision-making processes, including pattern recognition and episodic memory, can reduce the dependency of human-machine interactions on the abilities of a human user and can enable a machine to more closely emulate human-like responses. Such a cognitive model can enable digital technology to use cognitive capacities fundamental to human-like communication and cooperation to interact with humans.

Distributed Pheromone-Based Swarming Control of Unmanned Air and Ground Vehicles for RSTA

DTIC Science & Technology

2008-03-20

Forthcoming in Proceedings of SPIE Defense & Security Conference, March 2008, Orlando, FL Distributed Pheromone -Based Swarming Control of Unmanned...describes recent advances in a fully distributed digital pheromone algorithm that has demonstrated its effectiveness in managing the complexity of...onboard digital pheromone responding to the needs of the automatic target recognition algorithms. UAVs and UGVs controlled by the same pheromone algorithm

Digital Images and Human Vision

NASA Technical Reports Server (NTRS)

Watson, Andrew B.; Null, Cynthia H. (Technical Monitor)

1997-01-01

Processing of digital images destined for visual consumption raises many interesting questions regarding human visual sensitivity. This talk will survey some of these questions, including some that have been answered and some that have not. There will be an emphasis upon visual masking, and a distinction will be drawn between masking due to contrast gain control processes, and due to processes such as hypothesis testing, pattern recognition, and visual search.

A microcomputer interface for a digital audio processor-based data recording system.

PubMed

Croxton, T L; Stump, S J; Armstrong, W M

1987-10-01

An inexpensive interface is described that performs direct transfer of digitized data from the digital audio processor and video cassette recorder based data acquisition system designed by Bezanilla (1985, Biophys. J., 47:437-441) to an IBM PC/XT microcomputer. The FORTRAN callable software that drives this interface is capable of controlling the video cassette recorder and starting data collection immediately after recognition of a segment of previously collected data. This permits piecewise analysis of long intervals of data that would otherwise exceed the memory capability of the microcomputer.

Digital Archiving: Where the Past Lives Again

NASA Astrophysics Data System (ADS)

Paxson, K. B.

2012-06-01

The process of digital archiving for variable star data by manual entry with an Excel spreadsheet is described. Excel-based tools including a Step Magnitude Calculator and a Julian Date Calculator for variable star observations where magnitudes and Julian dates have not been reduced are presented. Variable star data in the literature and the AAVSO International Database prior to 1911 are presented and reviewed, with recent archiving work being highlighted. Digitization using optical character recognition software conversion is also demonstrated, with editing and formatting suggestions for the OCR-converted text.

A microcomputer interface for a digital audio processor-based data recording system.

PubMed Central

Croxton, T L; Stump, S J; Armstrong, W M

1987-01-01

An inexpensive interface is described that performs direct transfer of digitized data from the digital audio processor and video cassette recorder based data acquisition system designed by Bezanilla (1985, Biophys. J., 47:437-441) to an IBM PC/XT microcomputer. The FORTRAN callable software that drives this interface is capable of controlling the video cassette recorder and starting data collection immediately after recognition of a segment of previously collected data. This permits piecewise analysis of long intervals of data that would otherwise exceed the memory capability of the microcomputer. PMID:3676444

Rapid communication: Global-local processing affects recognition of distractor emotional faces.

PubMed

Srinivasan, Narayanan; Gupta, Rashmi

2011-03-01

Recent studies have shown links between happy faces and global, distributed attention as well as sad faces to local, focused attention. Emotions have been shown to affect global-local processing. Given that studies on emotion-cognition interactions have not explored the effect of perceptual processing at different spatial scales on processing stimuli with emotional content, the present study investigated the link between perceptual focus and emotional processing. The study investigated the effects of global-local processing on the recognition of distractor faces with emotional expressions. Participants performed a digit discrimination task with digits at either the global level or the local level presented against a distractor face (happy or sad) as background. The results showed that global processing associated with broad scope of attention facilitates recognition of happy faces, and local processing associated with narrow scope of attention facilitates recognition of sad faces. The novel results of the study provide conclusive evidence for emotion-cognition interactions by demonstrating the effect of perceptual processing on emotional faces. The results along with earlier complementary results on the effect of emotion on global-local processing support a reciprocal relationship between emotional processing and global-local processing. Distractor processing with emotional information also has implications for theories of selective attention.

Optical computing and neural networks; Proceedings of the Meeting, National Chiao Tung Univ., Hsinchu, Taiwan, Dec. 16, 17, 1992

NASA Technical Reports Server (NTRS)

Hsu, Ken-Yuh (Editor); Liu, Hua-Kuang (Editor)

1992-01-01

The present conference discusses optical neural networks, photorefractive nonlinear optics, optical pattern recognition, digital and analog processors, and holography and its applications. Attention is given to bifurcating optical information processing, neural structures in digital halftoning, an exemplar-based optical neural net classifier for color pattern recognition, volume storage in photorefractive disks, and microlaser-based compact optical neuroprocessors. Also treated are the optical implementation of a feature-enhanced optical interpattern-associative neural network model and its optical implementation, an optical pattern binary dual-rail logic gate module, a theoretical analysis for holographic associative memories, joint transform correlators, image addition and subtraction via the Talbot effect, and optical wavelet-matched filters. (No individual items are abstracted in this volume)

Optical computing and neural networks; Proceedings of the Meeting, National Chiao Tung Univ., Hsinchu, Taiwan, Dec. 16, 17, 1992

NASA Astrophysics Data System (ADS)

Hsu, Ken-Yuh; Liu, Hua-Kuang

The present conference discusses optical neural networks, photorefractive nonlinear optics, optical pattern recognition, digital and analog processors, and holography and its applications. Attention is given to bifurcating optical information processing, neural structures in digital halftoning, an exemplar-based optical neural net classifier for color pattern recognition, volume storage in photorefractive disks, and microlaser-based compact optical neuroprocessors. Also treated are the optical implementation of a feature-enhanced optical interpattern-associative neural network model and its optical implementation, an optical pattern binary dual-rail logic gate module, a theoretical analysis for holographic associative memories, joint transform correlators, image addition and subtraction via the Talbot effect, and optical wavelet-matched filters. (No individual items are abstracted in this volume)

DESIGN OF A PATTERN RECOGNITION DIGITAL COMPUTER WITH APPLICATION TO THE AUTOMATIC SCANNING OF BUBBLE CHAMBER NEGATIVES

DOE Office of Scientific and Technical Information (OSTI.GOV)

McCormick, B.H.; Narasimhan, R.

1963-01-01

The overall computer system contains three main parts: an input device, a pattern recognition unit (PRU), and a control computer. The bubble chamber picture is divided into a grid of st run. Concent 1-mm squares on the film. It is then processed in parallel in a two-dimensional array of 1024 identical processing modules (stalactites) of the PRU. The array can function as a two- dimensional shift register in which results of successive shifting operations can be accumulated. The pattern recognition process is generally controlled by a conventional arithmetic computer. (A.G.W.)

Pattern recognition and feature extraction with an optical Hough transform

NASA Astrophysics Data System (ADS)

Fernández, Ariel

2016-09-01

Pattern recognition and localization along with feature extraction are image processing applications of great interest in defect inspection and robot vision among others. In comparison to purely digital methods, the attractiveness of optical processors for pattern recognition lies in their highly parallel operation and real-time processing capability. This work presents an optical implementation of the generalized Hough transform (GHT), a well-established technique for the recognition of geometrical features in binary images. Detection of a geometric feature under the GHT is accomplished by mapping the original image to an accumulator space; the large computational requirements for this mapping make the optical implementation an attractive alternative to digital- only methods. Starting from the integral representation of the GHT, it is possible to device an optical setup where the transformation is obtained, and the size and orientation parameters can be controlled, allowing for dynamic scale and orientation-variant pattern recognition. A compact system for the above purposes results from the use of an electrically tunable lens for scale control and a rotating pupil mask for orientation variation, implemented on a high-contrast spatial light modulator (SLM). Real-time (as limited by the frame rate of the device used to capture the GHT) can also be achieved, allowing for the processing of video sequences. Besides, by thresholding of the GHT (with the aid of another SLM) and inverse transforming (which is optically achieved in the incoherent system under appropriate focusing setting), the previously detected features of interest can be extracted.

Analysis and Recognition of Curve Type as The Basis of Object Recognition in Image

NASA Astrophysics Data System (ADS)

Nugraha, Nurma; Madenda, Sarifuddin; Indarti, Dina; Dewi Agushinta, R.; Ernastuti

2016-06-01

An object in an image when analyzed further will show the characteristics that distinguish one object with another object in an image. Characteristics that are used in object recognition in an image can be a color, shape, pattern, texture and spatial information that can be used to represent objects in the digital image. The method has recently been developed for image feature extraction on objects that share characteristics curve analysis (simple curve) and use the search feature of chain code object. This study will develop an algorithm analysis and the recognition of the type of curve as the basis for object recognition in images, with proposing addition of complex curve characteristics with maximum four branches that will be used for the process of object recognition in images. Definition of complex curve is the curve that has a point of intersection. By using some of the image of the edge detection, the algorithm was able to do the analysis and recognition of complex curve shape well.

Automated thematic mapping and change detection of ERTS-A images. [digital interpretation of Arizona imagery

NASA Technical Reports Server (NTRS)

Gramenopoulos, N. (Principal Investigator)

1973-01-01

The author has identified the following significant results. For the recognition of terrain types, spatial signatures are developed from the diffraction patterns of small areas of ERTS-1 images. This knowledge is exploited for the measurements of a small number of meaningful spatial features from the digital Fourier transforms of ERTS-1 image cells containing 32 x 32 picture elements. Using these spatial features and a heuristic algorithm, the terrain types in the vicinity of Phoenix, Arizona were recognized by the computer with a high accuracy. Then, the spatial features were combined with spectral features and using the maximum likelihood criterion the recognition accuracy of terrain types increased substantially. It was determined that the recognition accuracy with the maximum likelihood criterion depends on the statistics of the feature vectors. Nonlinear transformations of the feature vectors are required so that the terrain class statistics become approximately Gaussian. It was also determined that for a given geographic area the statistics of the classes remain invariable for a period of a month but vary substantially between seasons.

Hooked on You.

ERIC Educational Resources Information Center

Sevier, Robert

1988-01-01

Most successful yield strategies use a series of messages specifically designed to meet the informational and emotional needs of students in the final decision-making stages. Techniques to try include: brochures, videotapes, handwritten postscripts, posters, and phone campaigns. (MLW)

License Plate Recognition System for Indian Vehicles

NASA Astrophysics Data System (ADS)

Sanap, P. R.; Narote, S. P.

2010-11-01

We consider the task of recognition of Indian vehicle number plates (also called license plates or registration plates in other countries). A system for Indian number plate recognition must cope with wide variations in the appearance of the plates. Each state uses its own range of designs with font variations between the designs. Also, vehicle owners may place the plates inside glass covered frames or use plates made of nonstandard materials. These issues compound the complexity of automatic number plate recognition, making existing approaches inadequate. We have developed a system that incorporates a novel combination of image processing and artificial neural network technologies to successfully locate and read Indian vehicle number plates in digital images. Commercial application of the system is envisaged.

Processing Of Binary Images

NASA Astrophysics Data System (ADS)

Hou, H. S.

1985-07-01

An overview of the recent progress in the area of digital processing of binary images in the context of document processing is presented here. The topics covered include input scan, adaptive thresholding, halftoning, scaling and resolution conversion, data compression, character recognition, electronic mail, digital typography, and output scan. Emphasis has been placed on illustrating the basic principles rather than descriptions of a particular system. Recent technology advances and research in this field are also mentioned.

Font group identification using reconstructed fonts

NASA Astrophysics Data System (ADS)

Cutter, Michael P.; van Beusekom, Joost; Shafait, Faisal; Breuel, Thomas M.

2011-01-01

Ideally, digital versions of scanned documents should be represented in a format that is searchable, compressed, highly readable, and faithful to the original. These goals can theoretically be achieved through OCR and font recognition, re-typesetting the document text with original fonts. However, OCR and font recognition remain hard problems, and many historical documents use fonts that are not available in digital forms. It is desirable to be able to reconstruct fonts with vector glyphs that approximate the shapes of the letters that form a font. In this work, we address the grouping of tokens in a token-compressed document into candidate fonts. This permits us to incorporate font information into token-compressed images even when the original fonts are unknown or unavailable in digital format. This paper extends previous work in font reconstruction by proposing and evaluating an algorithm to assign a font to every character within a document. This is a necessary step to represent a scanned document image with a reconstructed font. Through our evaluation method, we have measured a 98.4% accuracy for the assignment of letters to candidate fonts in multi-font documents.

Automatic target recognition apparatus and method

DOEpatents

Baumgart, Chris W.; Ciarcia, Christopher A.

2000-01-01

An automatic target recognition apparatus (10) is provided, having a video camera/digitizer (12) for producing a digitized image signal (20) representing an image containing therein objects which objects are to be recognized if they meet predefined criteria. The digitized image signal (20) is processed within a video analysis subroutine (22) residing in a computer (14) in a plurality of parallel analysis chains such that the objects are presumed to be lighter in shading than the background in the image in three of the chains and further such that the objects are presumed to be darker than the background in the other three chains. In two of the chains the objects are defined by surface texture analysis using texture filter operations. In another two of the chains the objects are defined by background subtraction operations. In yet another two of the chains the objects are defined by edge enhancement processes. In each of the analysis chains a calculation operation independently determines an error factor relating to the probability that the objects are of the type which should be recognized, and a probability calculation operation combines the results of the analysis chains.

Fundamental remote sensing science research program. Part 1: Status report of the mathematical pattern recognition and image analysis project

NASA Technical Reports Server (NTRS)

Heydorn, R. D.

1984-01-01

The Mathematical Pattern Recognition and Image Analysis (MPRIA) Project is concerned with basic research problems related to the study of the Earth from remotely sensed measurement of its surface characteristics. The program goal is to better understand how to analyze the digital image that represents the spatial, spectral, and temporal arrangement of these measurements for purposing of making selected inference about the Earth.

The ACR-NEMA Digital Imaging And Communications Standard: Evolution, Overview And Implementation Considerations

NASA Astrophysics Data System (ADS)

Alzner, Edgar; Murphy, Laura

1986-06-01

The growing digital nature of radiology images led to a recognition that compatibility of communication between imaging, display and data storage devices of different modalities and different manufacturers is necessary. The ACR-NEMA Digital Imaging and Communications Standard Committee was formed to develop a communications standard for radiological images. This standard includes the overall structure of a communication message and the protocols for bi-directional communication using end-to-end connections. The evolution and rationale of the ACR-NEMA Digital Imaging and Communication Standard are described. An overview is provided and sane practical implementation considerations are discussed. PACS will became reality only if the medical community accepts and implements the ACR-NEMA Standard.

Recognition and inference of crevice processing on digitized paintings

NASA Astrophysics Data System (ADS)

Karuppiah, S. P.; Srivatsa, S. K.

2013-03-01

This paper is designed to detect and removal of cracks on digitized paintings. The cracks are detected by threshold. Afterwards, the thin dark brush strokes which have been misidentified as cracks are removed using Median radial basis function neural network on hue and saturation data, Semi-automatic procedure based on region growing. Finally, crack is filled using wiener filter. The paper is well designed in such a way that most of the cracks on digitized paintings have identified and removed. The paper % of betterment is 90%. This paper helps us to perform not only on digitized paintings but also the medical images and bmp images. This paper is implemented by Mat Lab.

Intellectual factors in false memories of patients with schizophrenia.

PubMed

Zhu, Bi; Chen, Chuansheng; Loftus, Elizabeth F; Dong, Qi; Lin, Chongde; Li, Jun

2018-07-01

The current study explored the intellectual factors in false memories of 139 patients with schizophrenia, using a recognition task and an IQ test. The full-scale IQ score of the participants ranged from 57 to 144 (M = 100, SD = 14). The full IQ score had a negative correlation with false recognition in patients with schizophrenia, and positive correlations with high-confidence true recognition and discrimination rates. Further analyses with the subtests' scores revealed that false recognition was negatively correlated with scores of performance IQ (and one of its subtests: picture arrangement), whereas true recognition was positively correlated with scores of verbal IQ (and two of its subtests: information and digit span). High-IQ patients had less false recognition (overall or high-confidence false recognition), more high-confidence true recognition, and higher discrimination abilities than those with low IQ. These findings contribute to a better understanding of the cognitive mechanism in false memory of patients with schizophrenia, and are of practical relevance to the evaluation of memory reliability in patients with different intellectual levels. Copyright © 2018 Elsevier B.V. All rights reserved.

THE RELIABILITY OF HAND-WRITTEN AND COMPUTERISED RECORDS OF BIRTH DATA COLLECTED AT BARAGWANATH HOSPITAL IN SOWETO

PubMed Central

Ellison, GTH; Richter, LM; de Wet, T; Harris, HE; Griesel, RD; McIntyre, JA

2007-01-01

This study examined the reliability of hand-written and computerised records of birth data collected during the Birth to Ten study at Baragwanath Hospital in Soweto. The reliability of record-keeping in hand-written obstetric and neonatal files was assessed by comparing duplicate records of six different variables abstracted from six different sections in these files. The reliability of computerised record-keeping was assessed by comparing the original hand-written record of each variable with records contained in the hospital’s computerised database. These data sets displayed similar levels of reliability which suggests that similar errors occurred when data were transcribed from one section of the files to the next, and from these files to the computerised database. In both sets of records reliability was highest for the categorical variable infant sex, and for those continuous variables (such as maternal age and gravidity) recorded with unambiguous units. Reliability was lower for continuous variables that could be recorded with different levels of precision (such as birth weight), those that were occasionally measured more than once, and those that could be measured using more than one measurement technique (such as gestational age). Reducing the number of times records are transcribed, categorising continuous variables, and standardising the techniques used for measuring and recording variables would improve the reliability of both hand-written and computerised data sets. OPSOMMING In hierdie studie is die betroubaarheid van handgeskrewe en gerekenariseerde rekords van ge boortedata ondersoek, wat versamel is gedurende die ‘Birth to Ten’ -studie aan die Baragwanath hospitaal in Soweto. Die betroubaarheid van handgeskrewe verloskundige en pasgeboortelike rekords is beoordeel deur duplikaatrekords op ses verskillende verander likes te vergelyk, wat onttrek is uit ses verskillende dele van die betrokke lêers. Die gerekenariseerde rekords se betroubaarheid is beoordeel deur die oorspronklike geskrewe rekord van elke veranderlike te vergelyk met rekords wat beskikbaar is in die hospitaal se gerekenariseerde databasis Hierdie datastelle her vergelykbare vlakke van betroubaarheid getoon, waaruit afgelei kan word dat soortgelyke foute voorkom warmeer data oorgeplaas word vaneen deeivan ’n lêer na ’n ander, en vanaf die lêer na die gerekenariseerde databasis. In albei stelle rekords was die betroubaarheid die hoogste vir die kategoriese veranderlike suigeling se geslag, en vir daardie kontinue veranderlikes (soos moeder se ouderdom en gravida) wat in terme van ondubbelsinmge eenhede gekodeer kan word. Kontinue veranderlikes wat op wisselende vlakke van akkuratheid gemeet word (soos gewig met geboorte), veranderlikes wat soms meer as een keer gemeet is, en veranderlikes wat voigens meer as een metingstegniek bepaal is (soos draagtydsouderdom), was minder betroubaar Deur die aantal kere wat rekords oorgeskryf moet word te verminder, kontinue veranderlikes tat kategoriese veranderlikes te wysig. en tegnieke vir meting en aantekening van veranderlikes te standardiseer, kan die betroubaarheid van sowel handgeskrewe as gerekenariseerde datastelle verbeter word. PMID:9287552

Review of integrated digital systems: evolution and adoption

NASA Astrophysics Data System (ADS)

Fritz, Lawrence W.

The factors that are influencing the evolution of photogrammetric and remote sensing technology to transition into fully integrated digital systems are reviewed. These factors include societal pressures for new, more timely digital products from the Spatial Information Sciencesand the adoption of rapid technological advancements in digital processing hardware and software. Current major developments in leading government mapping agencies of the USA, such as the Digital Production System (DPS) modernization programme at the Defense Mapping Agency, and the Automated Nautical Charting System II (ANCS-II) programme and Integrated Digital Photogrammetric Facility (IDPF) at NOAA/National Ocean Service, illustrate the significant benefits to be realized. These programmes are examples of different levels of integrated systems that have been designed to produce digital products. They provide insights to the management complexities to be considered for very large integrated digital systems. In recognition of computer industry trends, a knowledge-based architecture for managing the complexity of the very large spatial information systems of the future is proposed.

Digital holographic-based cancellable biometric for personal authentication

NASA Astrophysics Data System (ADS)

Verma, Gaurav; Sinha, Aloka

2016-05-01

In this paper, we propose a new digital holographic-based cancellable biometric scheme for personal authentication and verification. The realization of cancellable biometric is presented by using an optoelectronic experimental approach, in which an optically recorded hologram of the fingerprint of a person is numerically reconstructed. Each reconstructed feature has its own perspective, which is utilized to generate user-specific fingerprint features by using a feature-extraction process. New representations of the user-specific fingerprint features can be obtained from the same hologram, by changing the reconstruction distance (d) by an amount Δd between the recording plane and the reconstruction plane. This parameter is the key to make the cancellable user-specific fingerprint features using a digital holographic technique, which allows us to choose different reconstruction distances when reissuing the user-specific fingerprint features in the event of compromise. We have shown theoretically that each user-specific fingerprint feature has a unique identity with a high discrimination ability, and the chances of a match between them are minimal. In this aspect, a recognition system has also been demonstrated using the fingerprint biometric of the enrolled person at a particular reconstruction distance. For the performance evaluation of a fingerprint recognition system—the false acceptance ratio, the false rejection ratio and the equal error rate are calculated using correlation. The obtained results show good discrimination ability between the genuine and the impostor populations with the highest recognition rate of 98.23%.

77 FR 57089 - Meeting of the Chronic Fatigue Syndrome Advisory Committee

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-17

..., 20201. Mailed testimony must be received no later than Monday, September 24, 2012. Note: PDF files, hand-written notes and photographs will not be accepted. Requests for public comment and written testimony will...

77 FR 31856 - Meeting of the Chronic Fatigue Syndrome Advisory Committee

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-30

..., 12 point font. Note: PDF files, hand-written notes and photographs will not be accepted. Requests for public comment and written testimony will not be accepted through the CFSAC mailbox. Also, the CFSAC...

[Patient safety: a comparison between handwritten and computerized voluntary incident reporting].

PubMed

Capucho, Helaine Carneiro; Arnas, Emilly Rasquini; Cassiani, Silvia Helena De Bortoli

2013-03-01

This study's objective was to compare two types of voluntary incident reporting methods that affect patient safety, handwritten (HR) and computerized (CR), in relation to the number of reports, type of incident reported the individual submitting the report, and quality of reports. This was a descriptive, retrospective and cross-sectional study. CR were more frequent than HR (61.2% vs. 38.6%) among the 1,089 reports analyzed and were submitted every day of the month, while HR were submitted only on weekdays. The highest number of reports referred to medication, followed by problems related to medical-hospital material and the professional who most frequently submitted reports were nurses in both cases. Overall CR presented higher quality than HR (86.1% vs. 61.7%); 36.8% of HR were illegible, a problem that was eliminated in CR. Therefore, the use of computerized incident reporting in hospitals favors qualified voluntary reports, increasing patient safety.

Historical Analyses of Disordered Handwriting

PubMed Central

Schiegg, Markus; Thorpe, Deborah

2016-01-01

Handwritten texts carry significant information, extending beyond the meaning of their words. Modern neurology, for example, benefits from the interpretation of the graphic features of writing and drawing for the diagnosis and monitoring of diseases and disorders. This article examines how handwriting analysis can be used, and has been used historically, as a methodological tool for the assessment of medical conditions and how this enhances our understanding of historical contexts of writing. We analyze handwritten material, writing tests and letters, from patients in an early 20th-century psychiatric hospital in southern Germany (Irsee/Kaufbeuren). In this institution, early psychiatrists assessed handwriting features, providing us novel insights into the earliest practices of psychiatric handwriting analysis, which can be connected to Berkenkotter’s research on medical admission records. We finally consider the degree to which historical handwriting bears semiotic potential to explain the psychological state and personality of a writer, and how future research in written communication should approach these sources. PMID:28408774

Nearshore coastal bathymetry data collected in 2016 from West Ship Island to Horn Island, Gulf Islands National Seashore, Mississippi

USGS Publications Warehouse

DeWitt, Nancy T.; Stalk, Chelsea A.; Fredericks, Jake J.; Flocks, James G.; Kelso, Kyle W.; Farmer, Andrew S.; Tuten, Thomas M.; Buster, Noreen A.

2018-04-13

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center, in cooperation with the U.S. Army Corps of Engineers, Mobile District, conducted bathymetric surveys of the nearshore waters surrounding Ship and Horn Islands, Gulf Islands National Seashore, Mississippi. The objective of this study was to establish base-level elevation conditions around West Ship, East Ship, and Horn Islands and their associated active littoral system prior to restoration activities. These activities include the closure of Camille Cut and the placement of sediment in the littoral zone of East Ship Island. These surveys can be compared with future surveys to monitor sediment migration patterns post-restoration and can also be measured against historic bathymetric datasets to further our understanding of island evolution.The USGS collected 667 line-kilometers (km) of single-beam bathymetry data and 844 line-km of interferometric swath bathymetry data in July 2016 under Field Activity Number 2016-347-FA. Data are provided in three datums: (1) the International Terrestrial Reference Frame of 2000 (ellipsoid height); (2) the North American Datum of 1983 (NAD83) CORS96 realization and the North American Vertical Datum of 1988 with respect to the GEOID12B model (orthometric height); and (3) NAD83 (CORS96) and Mean Lower Low Water (tidal datum). Data products, including x,y,zpoint datasets, trackline shapefiles, digital and handwritten Field Activity Collection Systems logs, 50-meter digital elevation model, and formal Federal Geographic Data Committee metadata, are available for download.

Scalable hybrid computation with spikes.

PubMed

Sarpeshkar, Rahul; O'Halloran, Micah

2002-09-01

We outline a hybrid analog-digital scheme for computing with three important features that enable it to scale to systems of large complexity: First, like digital computation, which uses several one-bit precise logical units to collectively compute a precise answer to a computation, the hybrid scheme uses several moderate-precision analog units to collectively compute a precise answer to a computation. Second, frequent discrete signal restoration of the analog information prevents analog noise and offset from degrading the computation. And, third, a state machine enables complex computations to be created using a sequence of elementary computations. A natural choice for implementing this hybrid scheme is one based on spikes because spike-count codes are digital, while spike-time codes are analog. We illustrate how spikes afford easy ways to implement all three components of scalable hybrid computation. First, as an important example of distributed analog computation, we show how spikes can create a distributed modular representation of an analog number by implementing digital carry interactions between spiking analog neurons. Second, we show how signal restoration may be performed by recursive spike-count quantization of spike-time codes. And, third, we use spikes from an analog dynamical system to trigger state transitions in a digital dynamical system, which reconfigures the analog dynamical system using a binary control vector; such feedback interactions between analog and digital dynamical systems create a hybrid state machine (HSM). The HSM extends and expands the concept of a digital finite-state-machine to the hybrid domain. We present experimental data from a two-neuron HSM on a chip that implements error-correcting analog-to-digital conversion with the concurrent use of spike-time and spike-count codes. We also present experimental data from silicon circuits that implement HSM-based pattern recognition using spike-time synchrony. We outline how HSMs may be used to perform learning, vector quantization, spike pattern recognition and generation, and how they may be reconfigured.

Evaluation of Local Media Surveillance for Improved Disease Recognition and Monitoring in Global Hotspot Regions

PubMed Central

Schwind, Jessica S.; Wolking, David J.; Brownstein, John S.; Mazet, Jonna A. K.; Smith, Woutrina A.

2014-01-01

Digital disease detection tools are technologically sophisticated, but dependent on digital information, which for many areas suffering from high disease burdens is simply not an option. In areas where news is often reported in local media with no digital counterpart, integration of local news information with digital surveillance systems, such as HealthMap (Boston Children’s Hospital), is critical. Little research has been published in regards to the specific contribution of local health-related articles to digital surveillance systems. In response, the USAID PREDICT project implemented a local media surveillance (LMS) pilot study in partner countries to monitor disease events reported in print media. This research assessed the potential of LMS to enhance digital surveillance reach in five low- and middle-income countries. Over 16 weeks, select surveillance system attributes of LMS, such as simplicity, flexibility, acceptability, timeliness, and stability were evaluated to identify strengths and weaknesses in the surveillance method. Findings revealed that LMS filled gaps in digital surveillance network coverage by contributing valuable localized information on disease events to the global HealthMap database. A total of 87 health events were reported through the LMS pilot in the 16-week monitoring period, including 71 unique reports not found by the HealthMap digital detection tool. Furthermore, HealthMap identified an additional 236 health events outside of LMS. It was also observed that belief in the importance of the project and proper source selection from the participants was crucial to the success of this method. The timely identification of disease outbreaks near points of emergence and the recognition of risk factors associated with disease occurrence continue to be important components of any comprehensive surveillance system for monitoring disease activity across populations. The LMS method, with its minimal resource commitment, could be one tool used to address the information gaps seen in global ‘hot spot’ regions. PMID:25333618

Limitations and requirements of content-based multimedia authentication systems

NASA Astrophysics Data System (ADS)

Wu, Chai W.

2001-08-01

Recently, a number of authentication schemes have been proposed for multimedia data such as images and sound data. They include both label based systems and semifragile watermarks. The main requirement for such authentication systems is that minor modifications such as lossy compression which do not alter the content of the data preserve the authenticity of the data, whereas modifications which do modify the content render the data not authentic. These schemes can be classified into two main classes depending on the model of image authentication they are based on. One of the purposes of this paper is to look at some of the advantages and disadvantages of these image authentication schemes and their relationship with fundamental limitations of the underlying model of image authentication. In particular, we study feature-based algorithms which generate an authentication tag based on some inherent features in the image such as the location of edges. The main disadvantage of most proposed feature-based algorithms is that similar images generate similar features, and therefore it is possible for a forger to generate dissimilar images that have the same features. On the other hand, the class of hash-based algorithms utilizes a cryptographic hash function or a digital signature scheme to reduce the data and generate an authentication tag. It inherits the security of digital signatures to thwart forgery attacks. The main disadvantage of hash-based algorithms is that the image needs to be modified in order to be made authenticatable. The amount of modification is on the order of the noise the image can tolerate before it is rendered inauthentic. The other purpose of this paper is to propose a multimedia authentication scheme which combines some of the best features of both classes of algorithms. The proposed scheme utilizes cryptographic hash functions and digital signature schemes and the data does not need to be modified in order to be made authenticatable. Several applications including the authentication of images on CD-ROM and handwritten documents will be discussed.

Digitizing Villanova University's Eclipsing Binary Card Catalogue

NASA Astrophysics Data System (ADS)

Guzman, Giannina; Dalton, Briana; Conroy, Kyle; Prsa, Andrej

2018-01-01

Villanova University’s Department of Astrophysics and Planetary Science has years of hand-written archival data on Eclipsing Binaries at its disposal. This card catalog began at Princeton in the 1930’s with notable contributions from scientists such as Henry Norris Russel. During World War II, the archive was moved to the University of Pennsylvania, which was one of the world centers for Eclipsing Binary research, consequently, the contributions to the catalog during this time were immense. It was then moved to University of Florida at Gainesville before being accepted by Villanova in the 1990’s. The catalog has been kept in storage since then. The objective of this project is to digitize this archive and create a fully functional online catalog that contains the information available on the cards, along with the scan of the actual cards. Our group has built a database using a python-powered infrastructure to contain the collected data. The team also built a prototype web-based searchable interface as a front-end to the catalog. Following the data-entry process, information like the Right Ascension and Declination will be run against SIMBAD and any differences between values will be noted as part of the catalog. Information published online from the card catalog and even discrepancies in information for a star, could be a catalyst for new studies on these Eclipsing Binaries. Once completed, the database-driven interface will be made available to astronomers worldwide. The group will also acquire, from the database, a list of referenced articles that have yet to be found online in order to further pursue their digitization. This list will be comprised of references in the cards that were neither found on ADS nor online during the data-entry process. Pursuing the integration of these references to online queries such as ADS will be an ongoing process that will contribute and further facilitate studies on Eclipsing Binaries.

Optical character recognition based on nonredundant correlation measurements.

PubMed

Braunecker, B; Hauck, R; Lohmann, A W

1979-08-15

The essence of character recognition is a comparison between the unknown character and a set of reference patterns. Usually, these reference patterns are all possible characters themselves, the whole alphabet in the case of letter characters. Obviously, N analog measurements are highly redundant, since only K = log(2)N binary decisions are enough to identify one out of N characters. Therefore, we devised K reference patterns accordingly. These patterns, called principal components, are found by digital image processing, but used in an optical analog computer. We will explain the concept of principal components, and we will describe experiments with several optical character recognition systems, based on this concept.

High confidence in falsely recognizing prototypical faces.

PubMed

Sampaio, Cristina; Reinke, Victoria; Mathews, Jeffrey; Swart, Alexandra; Wallinger, Stephen

2018-06-01

We applied a metacognitive approach to investigate confidence in recognition of prototypical faces. Participants were presented with sets of faces constructed digitally as deviations from prototype/base faces. Participants were then tested with a simple recognition task (Experiment 1) or a multiple-choice task (Experiment 2) for old and new items plus new prototypes, and they showed a high rate of confident false alarms to the prototypes. Confidence and accuracy relationship in this face recognition paradigm was found to be positive for standard items but negative for the prototypes; thus, it was contingent on the nature of the items used. The data have implications for lineups that employ match-to-suspect strategies.

Speech as a pilot input medium

NASA Technical Reports Server (NTRS)

Plummer, R. P.; Coler, C. R.

1977-01-01

The speech recognition system under development is a trainable pattern classifier based on a maximum-likelihood technique. An adjustable uncertainty threshold allows the rejection of borderline cases for which the probability of misclassification is high. The syntax of the command language spoken may be used as an aid to recognition, and the system adapts to changes in pronunciation if feedback from the user is available. Words must be separated by .25 second gaps. The system runs in real time on a mini-computer (PDP 11/10) and was tested on 120,000 speech samples from 10- and 100-word vocabularies. The results of these tests were 99.9% correct recognition for a vocabulary consisting of the ten digits, and 99.6% recognition for a 100-word vocabulary of flight commands, with a 5% rejection rate in each case. With no rejection, the recognition accuracies for the same vocabularies were 99.5% and 98.6% respectively.

Indoor navigation by image recognition

NASA Astrophysics Data System (ADS)

Choi, Io Teng; Leong, Chi Chong; Hong, Ka Wo; Pun, Chi-Man

2017-07-01

With the progress of smartphones hardware, it is simple on smartphone using image recognition technique such as face detection. In addition, indoor navigation system development is much slower than outdoor navigation system. Hence, this research proves a usage of image recognition technique for navigation in indoor environment. In this paper, we introduced an indoor navigation application that uses the indoor environment features to locate user's location and a route calculating algorithm to generate an appropriate path for user. The application is implemented on Android smartphone rather than iPhone. Yet, the application design can also be applied on iOS because the design is implemented without using special features only for Android. We found that digital navigation system provides better and clearer location information than paper map. Also, the indoor environment is ideal for Image recognition processing. Hence, the results motivate us to design an indoor navigation system using image recognition.

Improving the recognition of fingerprint biometric system using enhanced image fusion

NASA Astrophysics Data System (ADS)

Alsharif, Salim; El-Saba, Aed; Stripathi, Reshma

2010-04-01

Fingerprints recognition systems have been widely used by financial institutions, law enforcement, border control, visa issuing, just to mention few. Biometric identifiers can be counterfeited, but considered more reliable and secure compared to traditional ID cards or personal passwords methods. Fingerprint pattern fusion improves the performance of a fingerprint recognition system in terms of accuracy and security. This paper presents digital enhancement and fusion approaches that improve the biometric of the fingerprint recognition system. It is a two-step approach. In the first step raw fingerprint images are enhanced using high-frequency-emphasis filtering (HFEF). The second step is a simple linear fusion process between the raw images and the HFEF ones. It is shown that the proposed approach increases the verification and identification of the fingerprint biometric recognition system, where any improvement is justified using the correlation performance metrics of the matching algorithm.

Contribution of auditory working memory to speech understanding in mandarin-speaking cochlear implant users.

PubMed

Tao, Duoduo; Deng, Rui; Jiang, Ye; Galvin, John J; Fu, Qian-Jie; Chen, Bing

2014-01-01

To investigate how auditory working memory relates to speech perception performance by Mandarin-speaking cochlear implant (CI) users. Auditory working memory and speech perception was measured in Mandarin-speaking CI and normal-hearing (NH) participants. Working memory capacity was measured using forward digit span and backward digit span; working memory efficiency was measured using articulation rate. Speech perception was assessed with: (a) word-in-sentence recognition in quiet, (b) word-in-sentence recognition in speech-shaped steady noise at +5 dB signal-to-noise ratio, (c) Chinese disyllable recognition in quiet, (d) Chinese lexical tone recognition in quiet. Self-reported school rank was also collected regarding performance in schoolwork. There was large inter-subject variability in auditory working memory and speech performance for CI participants. Working memory and speech performance were significantly poorer for CI than for NH participants. All three working memory measures were strongly correlated with each other for both CI and NH participants. Partial correlation analyses were performed on the CI data while controlling for demographic variables. Working memory efficiency was significantly correlated only with sentence recognition in quiet when working memory capacity was partialled out. Working memory capacity was correlated with disyllable recognition and school rank when efficiency was partialled out. There was no correlation between working memory and lexical tone recognition in the present CI participants. Mandarin-speaking CI users experience significant deficits in auditory working memory and speech performance compared with NH listeners. The present data suggest that auditory working memory may contribute to CI users' difficulties in speech understanding. The present pattern of results with Mandarin-speaking CI users is consistent with previous auditory working memory studies with English-speaking CI users, suggesting that the lexical importance of voice pitch cues (albeit poorly coded by the CI) did not influence the relationship between working memory and speech perception.

Paediatric electronic infusion calculator: An intervention to eliminate infusion errors in paediatric critical care.

PubMed

Venkataraman, Aishwarya; Siu, Emily; Sadasivam, Kalaimaran

2016-11-01

Medication errors, including infusion prescription errors are a major public health concern, especially in paediatric patients. There is some evidence that electronic or web-based calculators could minimise these errors. To evaluate the impact of an electronic infusion calculator on the frequency of infusion errors in the Paediatric Critical Care Unit of The Royal London Hospital, London, United Kingdom. We devised an electronic infusion calculator that calculates the appropriate concentration, rate and dose for the selected medication based on the recorded weight and age of the child and then prints into a valid prescription chart. Electronic infusion calculator was implemented from April 2015 in Paediatric Critical Care Unit. A prospective study, five months before and five months after implementation of electronic infusion calculator, was conducted. Data on the following variables were collected onto a proforma: medication dose, infusion rate, volume, concentration, diluent, legibility, and missing or incorrect patient details. A total of 132 handwritten prescriptions were reviewed prior to electronic infusion calculator implementation and 119 electronic infusion calculator prescriptions were reviewed after electronic infusion calculator implementation. Handwritten prescriptions had higher error rate (32.6%) as compared to electronic infusion calculator prescriptions (<1%) with a p  < 0.001. Electronic infusion calculator prescriptions had no errors on dose, volume and rate calculation as compared to handwritten prescriptions, hence warranting very few pharmacy interventions. Use of electronic infusion calculator for infusion prescription significantly reduced the total number of infusion prescribing errors in Paediatric Critical Care Unit and has enabled more efficient use of medical and pharmacy time resources.

A dynamical pattern recognition model of gamma activity in auditory cortex

PubMed Central

Zavaglia, M.; Canolty, R.T.; Schofield, T.M.; Leff, A.P.; Ursino, M.; Knight, R.T.; Penny, W.D.

2012-01-01

This paper describes a dynamical process which serves both as a model of temporal pattern recognition in the brain and as a forward model of neuroimaging data. This process is considered at two separate levels of analysis: the algorithmic and implementation levels. At an algorithmic level, recognition is based on the use of Occurrence Time features. Using a speech digit database we show that for noisy recognition environments, these features rival standard cepstral coefficient features. At an implementation level, the model is defined using a Weakly Coupled Oscillator (WCO) framework and uses a transient synchronization mechanism to signal a recognition event. In a second set of experiments, we use the strength of the synchronization event to predict the high gamma (75–150 Hz) activity produced by the brain in response to word versus non-word stimuli. Quantitative model fits allow us to make inferences about parameters governing pattern recognition dynamics in the brain. PMID:22327049

Comparative Study of Nonlinear Time Warping Techniques in Isolated Word Speech Recognition Systems

DTIC Science & Technology

1981-06-17

all modules are loaded under a flexible research oriented supervisor, " Cicada ". Cicada allows for the integration of experimental ideas, extensions...evaluate alternate recognition methods. More detailed information about Cicada can be found in7 . In the following we limit our discussion to the design of...43.70 37.78 32.47 44.44 44.32 38 8. Figures Cicada - a flexible research oriented supervisor ReferenceSTernpl ates Front End Matching Digital Signal

Method of synthesized phase objects for pattern recognition with rotation invariance

NASA Astrophysics Data System (ADS)

Ostroukh, Alexander P.; Butok, Alexander M.; Shvets, Rostislav A.; Yezhov, Pavel V.; Kim, Jin-Tae; Kuzmenko, Alexander V.

2015-11-01

We present a development of the method of synthesized phase objects (SPO-method) [1] for the rotation-invariant pattern recognition. For the standard method of recognition and the SPO-method, the comparison of the parameters of correlation signals for a number of amplitude objects is executed at the realization of a rotation in an optical-digital correlator with the joint Fourier transformation. It is shown that not only the invariance relative to a rotation at a realization of the joint correlation for synthesized phase objects (SP-objects) but also the main advantage of the method of SP-objects over the reference one such as the unified δ-like recognition signal with the largest possible signal-to-noise ratio independent of the type of an object are attained.

Static sign language recognition using 1D descriptors and neural networks

NASA Astrophysics Data System (ADS)

Solís, José F.; Toxqui, Carina; Padilla, Alfonso; Santiago, César

2012-10-01

A frame work for static sign language recognition using descriptors which represents 2D images in 1D data and artificial neural networks is presented in this work. The 1D descriptors were computed by two methods, first one consists in a correlation rotational operator.1 and second is based on contour analysis of hand shape. One of the main problems in sign language recognition is segmentation; most of papers report a special color in gloves or background for hand shape analysis. In order to avoid the use of gloves or special clothing, a thermal imaging camera was used to capture images. Static signs were picked up from 1 to 9 digits of American Sign Language, a multilayer perceptron reached 100% recognition with cross-validation.

Start-ups Bring AI to Pathology.

PubMed

2018-04-01

New startups are developing pattern-recognition algorithms that could one day help pathologists more accurately spot tumors on digitized tissue images, thereby aiding in diagnosis, treatment, drug discovery, and more. ©2018 American Association for Cancer Research.

Quantum-assisted learning of graphical models with arbitrary pairwise connectivity

NASA Astrophysics Data System (ADS)

Realpe-Gómez, John; Benedetti, Marcello; Biswas, Rupak; Perdomo-Ortiz, Alejandro

Mainstream machine learning techniques rely heavily on sampling from generally intractable probability distributions. There is increasing interest in the potential advantages of using quantum computing technologies as sampling engines to speedup these tasks. However, some pressing challenges in state-of-the-art quantum annealers have to be overcome before we can assess their actual performance. The sparse connectivity, resulting from the local interaction between quantum bits in physical hardware implementations, is considered the most severe limitation to the quality of constructing powerful machine learning models. Here we show how to surpass this `curse of limited connectivity' bottleneck and illustrate our findings by training probabilistic generative models with arbitrary pairwise connectivity on a real dataset of handwritten digits and two synthetic datasets in experiments with up to 940 quantum bits. Our model can be trained in quantum hardware without full knowledge of the effective parameters specifying the corresponding Boltzmann-like distribution. Therefore, the need to infer the effective temperature at each iteration is avoided, speeding up learning, and the effect of noise in the control parameters is mitigated, improving accuracy. This work was supported in part by NASA, AFRL, ODNI, and IARPA.

A Quantitative Measure of Handwriting Dysfluency for Assessing Tardive Dyskinesia

PubMed Central

Caligiuri, Michael P.; Teulings, Hans-Leo; Dean, Charles E.; Lohr, James B.

2015-01-01

Tardive dyskinesia (TD) is movement disorder commonly associated with chronic exposure to antidopaminergic medications which may be in some cases disfiguring and socially disabling. The consensus from a growing body of research on the incidence and prevalence of TD in the modern era of antipsychotics indicates that this disorder has not disappeared continues to challenge the effective management of psychotic symptoms in patients with schizophrenia. A fundamental component in an effective strategy for managing TD is its reliable and accurate assessment. In the present study, we examined the clinical utility of a brief handwriting dysfluency measure for quantifying TD. Digitized samples of handwritten circles and loops were obtained from 62 psychosis patients with or without TD and from 50 healthy subjects. Two measures of dysfluent pen movements were extracted from each vertical pen stroke, including normalized jerk and the number of acceleration peaks. TD patients exhibited significantly higher dysfluency scores than non-TD patients and controls. Severity of handwriting movement dysfluency was correlated with AIMS severity ratings for some tasks. The procedure yielded high degrees of test-retest reliability. These results suggest that measures of handwriting movement dysfluency may be particularly useful for objectively evaluating the efficacy of pharmacotherapeutic strategies for treating TD. PMID:25679121

Too little, too late: reduced visual span and speed characterize pure alexia.

PubMed

Starrfelt, Randi; Habekost, Thomas; Leff, Alexander P

2009-12-01

Whether normal word reading includes a stage of visual processing selectively dedicated to word or letter recognition is highly debated. Characterizing pure alexia, a seemingly selective disorder of reading, has been central to this debate. Two main theories claim either that 1) Pure alexia is caused by damage to a reading specific brain region in the left fusiform gyrus or 2) Pure alexia results from a general visual impairment that may particularly affect simultaneous processing of multiple items. We tested these competing theories in 4 patients with pure alexia using sensitive psychophysical measures and mathematical modeling. Recognition of single letters and digits in the central visual field was impaired in all patients. Visual apprehension span was also reduced for both letters and digits in all patients. The only cortical region lesioned across all 4 patients was the left fusiform gyrus, indicating that this region subserves a function broader than letter or word identification. We suggest that a seemingly pure disorder of reading can arise due to a general reduction of visual speed and span, and explain why this has a disproportionate impact on word reading while recognition of other visual stimuli are less obviously affected.

Too Little, Too Late: Reduced Visual Span and Speed Characterize Pure Alexia

PubMed Central

Habekost, Thomas; Leff, Alexander P.

2009-01-01

Whether normal word reading includes a stage of visual processing selectively dedicated to word or letter recognition is highly debated. Characterizing pure alexia, a seemingly selective disorder of reading, has been central to this debate. Two main theories claim either that 1) Pure alexia is caused by damage to a reading specific brain region in the left fusiform gyrus or 2) Pure alexia results from a general visual impairment that may particularly affect simultaneous processing of multiple items. We tested these competing theories in 4 patients with pure alexia using sensitive psychophysical measures and mathematical modeling. Recognition of single letters and digits in the central visual field was impaired in all patients. Visual apprehension span was also reduced for both letters and digits in all patients. The only cortical region lesioned across all 4 patients was the left fusiform gyrus, indicating that this region subserves a function broader than letter or word identification. We suggest that a seemingly pure disorder of reading can arise due to a general reduction of visual speed and span, and explain why this has a disproportionate impact on word reading while recognition of other visual stimuli are less obviously affected. PMID:19366870

Top 10 "Smart" Technologies for Schools.

ERIC Educational Resources Information Center

Fodeman, Doug; Holzberg, Carol S.; Kennedy, Kristen; McIntire, Todd; McLester, Susan; Ohler, Jason; Parham, Charles; Poftak, Amy; Schrock, Kathy; Warlick, David

2002-01-01

Describes 10 smart technologies for education, including voice to text software; mobile computing; hybrid computing; virtual reality; artificial intelligence; telementoring; assessment methods; digital video production; fingerprint recognition; and brain functions. Lists pertinent Web sites for each technology. (LRW)

Theoretical Investigation of Optical Detection and Recognition of Single Biological Molecules Using Coherent Dynamics of Exciton-Plasmon Coupling

PubMed Central

Sadeghi, S. M.; Hood, B.; Patty, K. D.; Mao, C.-B.

2013-01-01

We use quantum coherence in a system consisting of one metallic nanorod and one semi-conductor quantum dot to investigate a plasmonic nanosensor capable of digital optical detection and recognition of single biological molecules. In such a sensor the adsorption of a specific molecule to the nanorod turns off the emission of the system when it interacts with an optical pulse having a certain intensity and temporal width. The proposed quantum sensors can count the number of molecules of the same type or differentiate between molecule types with digital optical signals that can be measured with high certainty. We show that these sensors are based on the ultrafast upheaval of coherent dynamics of the system and the removal of coherent blockage of energy transfer from the quantum dot to the nanorod once the adsorption process has occurred. PMID:24040424

Deep sea tests of a prototype of the KM3NeT digital optical module

NASA Astrophysics Data System (ADS)

Adrián-Martínez, S.; Ageron, M.; Aharonian, F.; Aiello, S.; Albert, A.; Ameli, F.; Anassontzis, E. G.; Anghinolfi, M.; Anton, G.; Anvar, S.; Ardid, M.; de Asmundis, R.; Balasi, K.; Band, H.; Barbarino, G.; Barbarito, E.; Barbato, F.; Baret, B.; Baron, S.; Belias, A.; Berbee, E.; van den Berg, A. M.; Berkien, A.; Bertin, V.; Beurthey, S.; van Beveren, V.; Beverini, N.; Biagi, S.; Bianucci, S.; Billault, M.; Birbas, A.; Boer Rookhuizen, H.; Bormuth, R.; Bouché, V.; Bouhadef, B.; Bourlis, G.; Bouwhuis, M.; Bozza, C.; Bruijn, R.; Brunner, J.; Cacopardo, G.; Caillat, L.; Calamai, M.; Calvo, D.; Capone, A.; Caramete, L.; Caruso, F.; Cecchini, S.; Ceres, A.; Cereseto, R.; Champion, C.; Château, F.; Chiarusi, T.; Christopoulou, B.; Circella, M.; Classen, L.; Cocimano, R.; Colonges, S.; Coniglione, R.; Cosquer, A.; Costa, M.; Coyle, P.; Creusot, A.; Curtil, C.; Cuttone, G.; D'Amato, C.; D'Amico, A.; De Bonis, G.; De Rosa, G.; Deniskina, N.; Destelle, J.-J.; Distefano, C.; Donzaud, C.; Dornic, D.; Dorosti-Hasankiadeh, Q.; Drakopoulou, E.; Drouhin, D.; Drury, L.; Durand, D.; Eberl, T.; Eleftheriadis, C.; Elsaesser, D.; Enzenhöfer, A.; Fermani, P.; Fusco, L. A.; Gajana, D.; Gal, T.; Galatà, S.; Gallo, F.; Garufi, F.; Gebyehu, M.; Giordano, V.; Gizani, N.; Gracia Ruiz, R.; Graf, K.; Grasso, R.; Grella, G.; Grmek, A.; Habel, R.; van Haren, H.; Heid, T.; Heijboer, A.; Heine, E.; Henry, S.; Hernández-Rey, J. J.; Herold, B.; Hevinga, M. A.; van der Hoek, M.; Hofestädt, J.; Hogenbirk, J.; Hugon, C.; Hößl, J.; Imbesi, M.; James, C.; Jansweijer, P.; Jochum, J.; de Jong, M.; Kadler, M.; Kalekin, O.; Kappes, A.; Kappos, E.; Katz, U.; Kavatsyuk, O.; Keller, P.; Kieft, G.; Koffeman, E.; Kok, H.; Kooijman, P.; Koopstra, J.; Korporaal, A.; Kouchner, A.; Koutsoukos, S.; Kreykenbohm, I.; Kulikovskiy, V.; Lahmann, R.; Lamare, P.; Larosa, G.; Lattuada, D.; Le Provost, H.; Leisos, A.; Lenis, D.; Leonora, E.; Lindsey Clark, M.; Liolios, A.; Llorens Alvarez, C. D.; Löhner, H.; Lo Presti, D.; Louis, F.; Maccioni, E.; Mannheim, K.; Manolopoulos, K.; Margiotta, A.; Mariş, O.; Markou, C.; Martínez-Mora, J. A.; Martini, A.; Masullo, R.; Michael, T.; Migliozzi, P.; Migneco, E.; Miraglia, A.; Mollo, C.; Mongelli, M.; Morganti, M.; Mos, S.; Moudden, Y.; Musico, P.; Musumeci, M.; Nicolaou, C.; Nicolau, C. A.; Orlando, A.; Orzelli, A.; Papageorgiou, K.; Papaikonomou, A.; Papaleo, R.; Păvălaş, G. E.; Peek, H.; Pellegrino, C.; Pellegriti, M. G.; Perrina, C.; Petridou, C.; Piattelli, P.; Pikounis, K.; Popa, V.; Pradier, Th.; Priede, M.; Pühlhofer, G.; Pulvirenti, S.; Racca, C.; Raffaelli, F.; Randazzo, N.; Rapidis, P. A.; Razis, P.; Real, D.; Resvanis, L.; Reubelt, J.; Riccobene, G.; Rovelli, A.; Royon, J.; Saldaña, M.; Samtleben, D. F. E.; Sanguineti, M.; Santangelo, A.; Sapienza, P.; Savvidis, I.; Schmelling, J.; Schnabel, J.; Sedita, M.; Seitz, T.; Sgura, I.; Simeone, F.; Siotis, I.; Sipala, V.; Solazzo, M.; Spitaleri, A.; Spurio, M.; Stavropoulos, G.; Steijger, J.; Stolarczyk, T.; Stransky, D.; Taiuti, M.; Terreni, G.; Tézier, D.; Théraube, S.; Thompson, L. F.; Timmer, P.; Trapierakis, H. I.; Trasatti, L.; Trovato, A.; Tselengidou, M.; Tsirigotis, A.; Tzamarias, S.; Tzamariudaki, E.; Vallage, B.; Van Elewyck, V.; Vermeulen, J.; Vernin, P.; Viola, S.; Vivolo, D.; Werneke, P.; Wiggers, L.; Wilms, J.; de Wolf, E.; van Wooning, R. H. L.; Yatkin, K.; Zachariadou, K.; Zonca, E.; Zornoza, J. D.; Zúñiga, J.; Zwart, A.

2014-09-01

The first prototype of a photo-detection unit of the future KM3NeT neutrino telescope has been deployed in the deep waters of the Mediterranean Sea. This digital optical module has a novel design with a very large photocathode area segmented by the use of 31 three inch photomultiplier tubes. It has been integrated in the ANTARES detector for in-situ testing and validation. This paper reports on the first months of data taking and rate measurements. The analysis results highlight the capabilities of the new module design in terms of background suppression and signal recognition. The directionality of the optical module enables the recognition of multiple Cherenkov photons from the same $^{40}$K decay and the localization bioluminescent activity in the neighbourhood. The single unit can cleanly identify atmospheric muons and provide sensitivity to the muon arrival directions.

Surface compositional mapping by spectral ratioing of ERTS-1 MSS data in the Wind River Basin and Range, Wyoming

NASA Technical Reports Server (NTRS)

Vincent, R. K. (Principal Investigator); Salmon, B. C.; Pillars, W. W.; Harris, J. E.

1975-01-01

The author has identified the following significant results. ERTS data collected in August and October 1972 were processed on digital and special purpose analog recognition computers using ratio enhancement and pattern recognition. Ratios of band-averaged laboratory reflectances of some minerals and rock types known to be in the scene compared favorably with ratios derived from the data by ratio normalization procedures. A single ratio display and density slice of the visible channels of ERTS MSS data, Channel 5/Channel 4 (R5,4), separated the Triassic Chugwater formation (redbeds) from other formations present and may have enhanced iron oxide minerals present at the surface in abundance. Comparison of data sets collected over the same area at two different times of the year by digital processing indicated that spectral variation due to environmental factors was reduced by ratio processing.

Engineering analysis of LANDSAT 1 data for Southeast Asian agriculture

NASA Technical Reports Server (NTRS)

Mcnair, A. J.; Heydt, H. L.; Liang, T.; Levine, G. (Principal Investigator)

1976-01-01

The author has identified the following significant results. LANDSAT spatial resolution was estimated to be adequate, but barely so, for the purpose of detailed assessment of rice or site status. This was due to the spatially fine grain, heterogenous nature of most rice areas. Use of two spectral bands of digital data (MSS 5 and MSS 6 or 7) appeared to be adequate for site recognition and gross site status assessment. Spectral/temporal signatures were found to be more powerful than spectra signatures alone and virtually essential for most analyses of rice growth and rice sites in the Philippine environment. Two band, two date signatures were estimated to be adequate for most purposes, although good results were achieved using one band two- or four-date signatures. A radiometric resolution of 64 levels in each band was found adequate for the analyses of LANDSAT digital data for site recognition and gross site or rice growth assessment.

Digital-Electronic/Optical Apparatus Would Recognize Targets

NASA Technical Reports Server (NTRS)

Scholl, Marija S.

1994-01-01

Proposed automatic target-recognition apparatus consists mostly of digital-electronic/optical cross-correlator that processes infrared images of targets. Infrared images of unknown targets correlated quickly with images of known targets. Apparatus incorporates some features of correlator described in "Prototype Optical Correlator for Robotic Vision System" (NPO-18451), and some of correlator described in "Compact Optical Correlator" (NPO-18473). Useful in robotic system; to recognize and track infrared-emitting, moving objects as variously shaped hot workpieces on conveyor belt.

New technique for real-time distortion-invariant multiobject recognition and classification

NASA Astrophysics Data System (ADS)

Hong, Rutong; Li, Xiaoshun; Hong, En; Wang, Zuyi; Wei, Hongan

2001-04-01

A real-time hybrid distortion-invariant OPR system was established to make 3D multiobject distortion-invariant automatic pattern recognition. Wavelet transform technique was used to make digital preprocessing of the input scene, to depress the noisy background and enhance the recognized object. A three-layer backpropagation artificial neural network was used in correlation signal post-processing to perform multiobject distortion-invariant recognition and classification. The C-80 and NOA real-time processing ability and the multithread programming technology were used to perform high speed parallel multitask processing and speed up the post processing rate to ROIs. The reference filter library was constructed for the distortion version of 3D object model images based on the distortion parameter tolerance measuring as rotation, azimuth and scale. The real-time optical correlation recognition testing of this OPR system demonstrates that using the preprocessing, post- processing, the nonlinear algorithm os optimum filtering, RFL construction technique and the multithread programming technology, a high possibility of recognition and recognition rate ere obtained for the real-time multiobject distortion-invariant OPR system. The recognition reliability and rate was improved greatly. These techniques are very useful to automatic target recognition.

Digital imaging technology assessment: Digital document storage project

NASA Technical Reports Server (NTRS)

1989-01-01

An ongoing technical assessment and requirements definition project is examining the potential role of digital imaging technology at NASA's STI facility. The focus is on the basic components of imaging technology in today's marketplace as well as the components anticipated in the near future. Presented is a requirement specification for a prototype project, an initial examination of current image processing at the STI facility, and an initial summary of image processing projects at other sites. Operational imaging systems incorporate scanners, optical storage, high resolution monitors, processing nodes, magnetic storage, jukeboxes, specialized boards, optical character recognition gear, pixel addressable printers, communications, and complex software processes.

Psychometrically equivalent bisyllabic words for speech recognition threshold testing in Vietnamese.

PubMed

Harris, Richard W; McPherson, David L; Hanson, Claire M; Eggett, Dennis L

2017-08-01

This study identified, digitally recorded, edited and evaluated 89 bisyllabic Vietnamese words with the goal of identifying homogeneous words that could be used to measure the speech recognition threshold (SRT) in native talkers of Vietnamese. Native male and female talker productions of 89 Vietnamese bisyllabic words were recorded, edited and then presented at intensities ranging from -10 to 20 dBHL. Logistic regression was used to identify the best words for measuring the SRT. Forty-eight words were selected and digitally edited to have 50% intelligibility at a level equal to the mean pure-tone average (PTA) for normally hearing participants (5.2 dBHL). Twenty normally hearing native Vietnamese participants listened to and repeated bisyllabic Vietnamese words at intensities ranging from -10 to 20 dBHL. A total of 48 male and female talker recordings of bisyllabic words with steep psychometric functions (>9.0%/dB) were chosen for the final bisyllabic SRT list. Only words homogeneous with respect to threshold audibility with steep psychometric function slopes were chosen for the final list. Digital recordings of bisyllabic Vietnamese words are now available for use in measuring the SRT for patients whose native language is Vietnamese.

Noisy text categorization.

PubMed

Vinciarelli, Alessandro

2005-12-01

This work presents categorization experiments performed over noisy texts. By noisy, we mean any text obtained through an extraction process (affected by errors) from media other than digital texts (e.g., transcriptions of speech recordings extracted with a recognition system). The performance of a categorization system over the clean and noisy (Word Error Rate between approximately 10 and approximately 50 percent) versions of the same documents is compared. The noisy texts are obtained through handwriting recognition and simulation of optical character recognition. The results show that the performance loss is acceptable for Recall values up to 60-70 percent depending on the noise sources. New measures of the extraction process performance, allowing a better explanation of the categorization results, are proposed.

ASERA: A Spectrum Eye Recognition Assistant

NASA Astrophysics Data System (ADS)

Yuan, Hailong; Zhang, Haotong; Zhang, Yanxia; Lei, Yajuan; Dong, Yiqiao; Zhao, Yongheng

2018-04-01

ASERA, ASpectrum Eye Recognition Assistant, aids in quasar spectral recognition and redshift measurement and can also be used to recognize various types of spectra of stars, galaxies and AGNs (Active Galactic Nucleus). This interactive software allows users to visualize observed spectra, superimpose template spectra from the Sloan Digital Sky Survey (SDSS), and interactively access related spectral line information. ASERA is an efficient and user-friendly semi-automated toolkit for the accurate classification of spectra observed by LAMOST (the Large Sky Area Multi-object Fiber Spectroscopic Telescope) and is available as a standalone Java application and as a Java applet. The software offers several functions, including wavelength and flux scale settings, zoom in and out, redshift estimation, and spectral line identification.

Neural network-based systems for handprint OCR applications.

PubMed

Ganis, M D; Wilson, C L; Blue, J L

1998-01-01

Over the last five years or so, neural network (NN)-based approaches have been steadily gaining performance and popularity for a wide range of optical character recognition (OCR) problems, from isolated digit recognition to handprint recognition. We present an NN classification scheme based on an enhanced multilayer perceptron (MLP) and describe an end-to-end system for form-based handprint OCR applications designed by the National Institute of Standards and Technology (NIST) Visual Image Processing Group. The enhancements to the MLP are based on (i) neuron activations functions that reduce the occurrences of singular Jacobians; (ii) successive regularization to constrain the volume of the weight space; and (iii) Boltzmann pruning to constrain the dimension of the weight space. Performance characterization studies of NN systems evaluated at the first OCR systems conference and the NIST form-based handprint recognition system are also summarized.

Palmprint Recognition Across Different Devices.

PubMed

Jia, Wei; Hu, Rong-Xiang; Gui, Jie; Zhao, Yang; Ren, Xiao-Ming

2012-01-01

In this paper, the problem of Palmprint Recognition Across Different Devices (PRADD) is investigated, which has not been well studied so far. Since there is no publicly available PRADD image database, we created a non-contact PRADD image database containing 12,000 grayscale captured from 100 subjects using three devices, i.e., one digital camera and two smart-phones. Due to the non-contact image acquisition used, rotation and scale changes between different images captured from a same palm are inevitable. We propose a robust method to calculate the palm width, which can be effectively used for scale normalization of palmprints. On this PRADD image database, we evaluate the recognition performance of three different methods, i.e., subspace learning method, correlation method, and orientation coding based method, respectively. Experiments results show that orientation coding based methods achieved promising recognition performance for PRADD.

Palmprint Recognition across Different Devices

PubMed Central

Jia, Wei; Hu, Rong-Xiang; Gui, Jie; Zhao, Yang; Ren, Xiao-Ming

2012-01-01

In this paper, the problem of Palmprint Recognition Across Different Devices (PRADD) is investigated, which has not been well studied so far. Since there is no publicly available PRADD image database, we created a non-contact PRADD image database containing 12,000 grayscale captured from 100 subjects using three devices, i.e., one digital camera and two smart-phones. Due to the non-contact image acquisition used, rotation and scale changes between different images captured from a same palm are inevitable. We propose a robust method to calculate the palm width, which can be effectively used for scale normalization of palmprints. On this PRADD image database, we evaluate the recognition performance of three different methods, i.e., subspace learning method, correlation method, and orientation coding based method, respectively. Experiments results show that orientation coding based methods achieved promising recognition performance for PRADD. PMID:22969380

An effective approach for iris recognition using phase-based image matching.

PubMed

Miyazawa, Kazuyuki; Ito, Koichi; Aoki, Takafumi; Kobayashi, Koji; Nakajima, Hiroshi

2008-10-01

This paper presents an efficient algorithm for iris recognition using phase-based image matching--an image matching technique using phase components in 2D Discrete Fourier Transforms (DFTs) of given images. Experimental evaluation using CASIA iris image databases (versions 1.0 and 2.0) and Iris Challenge Evaluation (ICE) 2005 database clearly demonstrates that the use of phase components of iris images makes possible to achieve highly accurate iris recognition with a simple matching algorithm. This paper also discusses major implementation issues of our algorithm. In order to reduce the size of iris data and to prevent the visibility of iris images, we introduce the idea of 2D Fourier Phase Code (FPC) for representing iris information. The 2D FPC is particularly useful for implementing compact iris recognition devices using state-of-the-art Digital Signal Processing (DSP) technology.

Accurate, fast, and secure biometric fingerprint recognition system utilizing sensor fusion of fingerprint patterns

NASA Astrophysics Data System (ADS)

El-Saba, Aed; Alsharif, Salim; Jagapathi, Rajendarreddy

2011-04-01

Fingerprint recognition is one of the first techniques used for automatically identifying people and today it is still one of the most popular and effective biometric techniques. With this increase in fingerprint biometric uses, issues related to accuracy, security and processing time are major challenges facing the fingerprint recognition systems. Previous work has shown that polarization enhancementencoding of fingerprint patterns increase the accuracy and security of fingerprint systems without burdening the processing time. This is mainly due to the fact that polarization enhancementencoding is inherently a hardware process and does not have detrimental time delay effect on the overall process. Unpolarized images, however, posses a high visual contrast and when fused (without digital enhancement) properly with polarized ones, is shown to increase the recognition accuracy and security of the biometric system without any significant processing time delay.

5 CFR 850.103 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-01-01

... graphical image of a handwritten signature, usually created using a special computer input device, such as a... comparison with the characteristics and biometric data of a known or exemplar signature image. Director means... folder across the Government. Electronic retirement and insurance processing system means the new...

5 CFR 850.103 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... graphical image of a handwritten signature, usually created using a special computer input device, such as a... comparison with the characteristics and biometric data of a known or exemplar signature image. Director means... folder across the Government. Electronic retirement and insurance processing system means the new...

5 CFR 850.103 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-01-01

... graphical image of a handwritten signature, usually created using a special computer input device, such as a... comparison with the characteristics and biometric data of a known or exemplar signature image. Director means... folder across the Government. Electronic retirement and insurance processing system means the new...

32 CFR 637.13 - Retention of property.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 32 National Defense 4 2011-07-01 2011-07-01 false Retention of property. 637.13 Section 637.13 National Defense Department of Defense (Continued) DEPARTMENT OF THE ARMY (CONTINUED) LAW ENFORCEMENT AND.... Reports of investigation, photographs, exhibits, handwritten notes, sketches, and other materials...

32 CFR 637.13 - Retention of property.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 32 National Defense 4 2010-07-01 2010-07-01 true Retention of property. 637.13 Section 637.13 National Defense Department of Defense (Continued) DEPARTMENT OF THE ARMY (CONTINUED) LAW ENFORCEMENT AND.... Reports of investigation, photographs, exhibits, handwritten notes, sketches, and other materials...

Image processing for a tactile/vision substitution system using digital CNN.

PubMed

Lin, Chien-Nan; Yu, Sung-Nien; Hu, Jin-Cheng

2006-01-01

In view of the parallel processing and easy implementation properties of CNN, we propose to use digital CNN as the image processor of a tactile/vision substitution system (TVSS). The digital CNN processor is used to execute the wavelet down-sampling filtering and the half-toning operations, aiming to extract important features from the images. A template combination method is used to embed the two image processing functions into a single CNN processor. The digital CNN processor is implemented on an intellectual property (IP) and is implemented on a XILINX VIRTEX II 2000 FPGA board. Experiments are designated to test the capability of the CNN processor in the recognition of characters and human subjects in different environments. The experiments demonstrates impressive results, which proves the proposed digital CNN processor a powerful component in the design of efficient tactile/vision substitution systems for the visually impaired people.

A 128-channel Time-to-Digital Converter (TDC) inside a Virtex-5 FPGA on the GANDALF module

NASA Astrophysics Data System (ADS)

Büchele, M.; Fischer, H.; Gorzellik, M.; Herrmann, F.; Königsmann, K.; Schill, C.; Schopferer, S.

2012-03-01

The GANDALF 6U-VME64x/VXS module has been developed for the digitization and real time analysis of detector signals. To perform different applications such as analog-to-digital or time-to-digital conversions, coincidence matrix formation, fast pattern recognition and trigger generation, this module comes with exchangeable analog and digital mezzanine cards. Based on this platform, we present a 128-channel TDC which is implemented in a single Xilinx Virtex-5 FPGA using a shifted clock sampling method. In contrast to common TDC concepts, the input signal is sampled by 16 equidistant phase-shifted clocks. A particular challenge of the design is the minimum skew routing of the input signals to the sampling flip-flops. We present measurement results for the differential nonlinearity and the time resolution of the TDC readout system.

Localization and recognition of traffic signs for automated vehicle control systems

NASA Astrophysics Data System (ADS)

Zadeh, Mahmoud M.; Kasvand, T.; Suen, Ching Y.

1998-01-01

We present a computer vision system for detection and recognition of traffic signs. Such systems are required to assist drivers and for guidance and control of autonomous vehicles on roads and city streets. For experiments we use sequences of digitized photographs and off-line analysis. The system contains four stages. First, region segmentation based on color pixel classification called SRSM. SRSM limits the search to regions of interest in the scene. Second, we use edge tracing to find parts of outer edges of signs which are circular or straight, corresponding to the geometrical shapes of traffic signs. The third step is geometrical analysis of the outer edge and preliminary recognition of each candidate region, which may be a potential traffic sign. The final step in recognition uses color combinations within each region and model matching. This system maybe used for recognition of other types of objects, provided that the geometrical shape and color content remain reasonably constant. The method is reliable, easy to implement, and fast, This differs form the road signs recognition method in the PROMETEUS. The overall structure of the approach is sketched.

[Digital vs. analog hearing aids for children. Is there a method for making an objective comparison possible?].

PubMed

Prinz, I; Nubel, K; Gross, M

2002-09-01

Until now, the assumed benefits of digital hearing aids are reflected only in subjective descriptions by patients with hearing aids, but cannot be documented adequately by routine diagnostic methods. Seventeen schoolchildren with moderate severe bilateral symmetrical sensorineural hearing loss were examined in a double-blinded crossover study. Differences in performance between a fully digital hearing aid (DigiFocus compact/Oticon) and an analogous digitally programmable two-channel hearing aid were evaluated. Of the 17 children, 13 choose the digital and 4 the analogous hearing aid. In contrast to the clear subjective preferences for the fully digital hearing aid, we could not obtain any significant results with routine diagnostic methods. Using the "virtual hearing aid," a subjective comparison and speech recognition performance task yielded significant differences. The virtual hearing aid proved to be suitable for a direct comparison of different hearing aids and can be used for double-blind testing in a pediatric population.

Recognition and localization of speech by adult cochlear implant recipients wearing a digital hearing aid in the nonimplanted ear (bimodal hearing).

PubMed

Potts, Lisa G; Skinner, Margaret W; Litovsky, Ruth A; Strube, Michael J; Kuk, Francis

2009-06-01

The use of bilateral amplification is now common clinical practice for hearing aid users but not for cochlear implant recipients. In the past, most cochlear implant recipients were implanted in one ear and wore only a monaural cochlear implant processor. There has been recent interest in benefits arising from bilateral stimulation that may be present for cochlear implant recipients. One option for bilateral stimulation is the use of a cochlear implant in one ear and a hearing aid in the opposite nonimplanted ear (bimodal hearing). This study evaluated the effect of wearing a cochlear implant in one ear and a digital hearing aid in the opposite ear on speech recognition and localization. A repeated-measures correlational study was completed. Nineteen adult Cochlear Nucleus 24 implant recipients participated in the study. The participants were fit with a Widex Senso Vita 38 hearing aid to achieve maximum audibility and comfort within their dynamic range. Soundfield thresholds, loudness growth, speech recognition, localization, and subjective questionnaires were obtained six-eight weeks after the hearing aid fitting. Testing was completed in three conditions: hearing aid only, cochlear implant only, and cochlear implant and hearing aid (bimodal). All tests were repeated four weeks after the first test session. Repeated-measures analysis of variance was used to analyze the data. Significant effects were further examined using pairwise comparison of means or in the case of continuous moderators, regression analyses. The speech-recognition and localization tasks were unique, in that a speech stimulus presented from a variety of roaming azimuths (140 degree loudspeaker array) was used. Performance in the bimodal condition was significantly better for speech recognition and localization compared to the cochlear implant-only and hearing aid-only conditions. Performance was also different between these conditions when the location (i.e., side of the loudspeaker array that presented the word) was analyzed. In the bimodal condition, the speech-recognition and localization tasks were equal regardless of which side of the loudspeaker array presented the word, while performance was significantly poorer for the monaural conditions (hearing aid only and cochlear implant only) when the words were presented on the side with no stimulation. Binaural loudness summation of 1-3 dB was seen in soundfield thresholds and loudness growth in the bimodal condition. Measures of the audibility of sound with the hearing aid, including unaided thresholds, soundfield thresholds, and the Speech Intelligibility Index, were significant moderators of speech recognition and localization. Based on the questionnaire responses, participants showed a strong preference for bimodal stimulation. These findings suggest that a well-fit digital hearing aid worn in conjunction with a cochlear implant is beneficial to speech recognition and localization. The dynamic test procedures used in this study illustrate the importance of bilateral hearing for locating, identifying, and switching attention between multiple speakers. It is recommended that unilateral cochlear implant recipients, with measurable unaided hearing thresholds, be fit with a hearing aid.

Robust keyword retrieval method for OCRed text

NASA Astrophysics Data System (ADS)

Fujii, Yusaku; Takebe, Hiroaki; Tanaka, Hiroshi; Hotta, Yoshinobu

2011-01-01

Document management systems have become important because of the growing popularity of electronic filing of documents and scanning of books, magazines, manuals, etc., through a scanner or a digital camera, for storage or reading on a PC or an electronic book. Text information acquired by optical character recognition (OCR) is usually added to the electronic documents for document retrieval. Since texts generated by OCR generally include character recognition errors, robust retrieval methods have been introduced to overcome this problem. In this paper, we propose a retrieval method that is robust against both character segmentation and recognition errors. In the proposed method, the insertion of noise characters and dropping of characters in the keyword retrieval enables robustness against character segmentation errors, and character substitution in the keyword of the recognition candidate for each character in OCR or any other character enables robustness against character recognition errors. The recall rate of the proposed method was 15% higher than that of the conventional method. However, the precision rate was 64% lower.

Pc-based car license plate reading

NASA Astrophysics Data System (ADS)

Tanabe, Katsuyoshi; Marubayashi, Eisaku; Kawashima, Harumi; Nakanishi, Tadashi; Shio, Akio

1994-03-01

A PC-based car license plate recognition system has been developed. The system recognizes Chinese characters and Japanese phonetic hiragana characters as well as six digits on Japanese license plates. The system consists of a CCD camera, vehicle sensors, a strobe unit, a monitoring center, and an i486-based PC. The PC includes in its extension slots: a vehicle detector board, a strobe emitter board, and an image grabber board. When a passing vehicle is detected by the vehicle sensors, the strobe emits a pulse of light. The light pulse is synchronized with the time the vehicle image is frozen on an image grabber board. The recognition process is composed of three steps: image thresholding, character region extraction, and matching-based character recognition. The recognition software can handle obscured characters. Experimental results for hundreds of outdoor images showed high recognition performance within relatively short performance times. The results confirmed that the system is applicable to a wide variety of applications such as automatic vehicle identification and travel time measurement.

Sight-Word Practice in a Flash!

ERIC Educational Resources Information Center

Erwin, Robin W., Jr.

2016-01-01

For learners who need sight-word practice, including young students and struggling readers, digital flash cards may promote automatic word recognition when used as a supplemental activity to regular reading instruction. A novel use of common presentation software efficiently supports this practice strategy.

5 CFR 850.103 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-01-01

...) ELECTRONIC RETIREMENT PROCESSING General Provisions § 850.103 Definitions. In this part— Agency means an... graphical image of a handwritten signature usually created using a special computer input device (such as a... comparison with the characteristics and biometric data of a known or exemplar signature image. Director means...

Historical Analyses of Disordered Handwriting: Perspectives on Early 20th-Century Material From a German Psychiatric Hospital.

PubMed

Schiegg, Markus; Thorpe, Deborah

2017-01-01

Handwritten texts carry significant information, extending beyond the meaning of their words. Modern neurology, for example, benefits from the interpretation of the graphic features of writing and drawing for the diagnosis and monitoring of diseases and disorders. This article examines how handwriting analysis can be used, and has been used historically, as a methodological tool for the assessment of medical conditions and how this enhances our understanding of historical contexts of writing. We analyze handwritten material, writing tests and letters, from patients in an early 20th-century psychiatric hospital in southern Germany (Irsee/Kaufbeuren). In this institution, early psychiatrists assessed handwriting features, providing us novel insights into the earliest practices of psychiatric handwriting analysis, which can be connected to Berkenkotter's research on medical admission records. We finally consider the degree to which historical handwriting bears semiotic potential to explain the psychological state and personality of a writer, and how future research in written communication should approach these sources.

Atmospheric pressure MALDI for the noninvasive characterization of carbonaceous ink from Renaissance documents.

PubMed

Grasso, Giuseppe; Calcagno, Marzia; Rapisarda, Alessandro; D'Agata, Roberta; Spoto, Giuseppe

2017-06-01

The analytical methods that are usually applied to determine the compositions of inks from ancient manuscripts usually focus on inorganic components, as in the case of iron gall ink. In this work, we describe the use of atmospheric pressure/matrix-assisted laser desorption ionization-mass spectrometry (AP/MALDI-MS) as a spatially resolved analytical technique for the study of the organic carbonaceous components of inks used in handwritten parts of ancient books for the first time. Large polycyclic aromatic hydrocarbons (L-PAH) were identified in situ in the ink of XVII century handwritten documents. We prove that it is possible to apply MALDI-MS as a suitable microdestructive diagnostic tool for analyzing samples in air at atmospheric pressure, thus simplifying investigations of the organic components of artistic and archaeological objects. The interpretation of the experimental MS results was supported by independent Raman spectroscopic investigations. Graphical abstract Atmospheric pressure/MALDI mass spectrometry detects in situ polycyclic aromatic hydrocarbons in the carbonaceous ink of XVII century manuscripts.

Adults' strategies for simple addition and multiplication: verbal self-reports and the operand recognition paradigm.

PubMed

Metcalfe, Arron W S; Campbell, Jamie I D

2011-05-01

Accurate measurement of cognitive strategies is important in diverse areas of psychological research. Strategy self-reports are a common measure, but C. Thevenot, M. Fanget, and M. Fayol (2007) proposed a more objective method to distinguish different strategies in the context of mental arithmetic. In their operand recognition paradigm, speed of recognition memory for problem operands after solving a problem indexes strategy (e.g., direct memory retrieval vs. a procedural strategy). Here, in 2 experiments, operand recognition time was the same following simple addition or multiplication, but, consistent with a wide variety of previous research, strategy reports indicated much greater use of procedures (e.g., counting) for addition than multiplication. Operation, problem size (e.g., 2 + 3 vs. 8 + 9), and operand format (digits vs. words) had interactive effects on reported procedure use that were not reflected in recognition performance. Regression analyses suggested that recognition time was influenced at least as much by the relative difficulty of the preceding problem as by the strategy used. The findings indicate that the operand recognition paradigm is not a reliable substitute for strategy reports and highlight the potential impact of difficulty-related carryover effects in sequential cognitive tasks.