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Author : Judith Dixon-Otton
Publisher :
ISBN 13 :
Total Pages : 56 pages
Book Rating : 4.:/5 (33 download)
Download or read book A Neural Network Model for Optical Character Recognition written by Judith Dixon-Otton and published by . This book was released on 1994 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Hongbo He
Publisher :
ISBN 13 :
Total Pages : 196 pages
Book Rating : 4.:/5 (266 download)
Download or read book A Self-growing Neural Network Model for Optical Character Recognition written by Hongbo He and published by . This book was released on 1991 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Muhammad Sarfraz
Publisher : Engineering Science Reference
ISBN 13 : 9781799852049
Total Pages : pages
Book Rating : 4.8/5 (52 download)
Download or read book Advancements in Computer Vision Applications in Intelligent Systems and Multimedia Technologies written by Muhammad Sarfraz and published by Engineering Science Reference. This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book discusses innovative developments in computational imaging for solving real-life issues and problems and addresses their execution in various disciplines"--
Author : Mohamed Cheriet
Publisher : John Wiley & Sons
ISBN 13 : 9780470176528
Total Pages : 351 pages
Book Rating : 4.1/5 (765 download)
Download or read book Character Recognition Systems written by Mohamed Cheriet and published by John Wiley & Sons. This book was released on 2007-11-27 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Much of pattern recognition theory and practice, including methods such as Support Vector Machines, has emerged in an attempt to solve the character recognition problem. This book is written by very well-known academics who have worked in the field for many years and have made significant and lasting contributions. The book will no doubt be of value to students and practitioners." -Sargur N. Srihari, SUNY Distinguished Professor, Department of Computer Science and Engineering, and Director, Center of Excellence for Document Analysis and Recognition (CEDAR), University at Buffalo, The State University of New York "The disciplines of optical character recognition and document image analysis have a history of more than forty years. In the last decade, the importance and popularity of these areas have grown enormously. Surprisingly, however, the field is not well covered by any textbook. This book has been written by prominent leaders in the field. It includes all important topics in optical character recognition and document analysis, and is written in a very coherent and comprehensive style. This book satisfies an urgent need. It is a volume the community has been awaiting for a long time, and I can enthusiastically recommend it to everybody working in the area." -Horst Bunke, Professor, Institute of Computer Science and Applied Mathematics (IAM), University of Bern, Switzerland In Character Recognition Systems, the authors provide practitioners and students with the fundamental principles and state-of-the-art computational methods of reading printed texts and handwritten materials. The information presented is analogous to the stages of a computer recognition system, helping readers master the theory and latest methodologies used in character recognition in a meaningful way. This book covers: * Perspectives on the history, applications, and evolution of Optical Character Recognition (OCR) * The most widely used pre-processing techniques, as well as methods for extracting character contours and skeletons * Evaluating extracted features, both structural and statistical * Modern classification methods that are successful in character recognition, including statistical methods, Artificial Neural Networks (ANN), Support Vector Machines (SVM), structural methods, and multi-classifier methods * An overview of word and string recognition methods and techniques * Case studies that illustrate practical applications, with descriptions of the methods and theories behind the experimental results Each chapter contains major steps and tricks to handle the tasks described at-hand. Researchers and graduate students in computer science and engineering will find this book useful for designing a concrete system in OCR technology, while practitioners will rely on it as a valuable resource for the latest advances and modern technologies that aren't covered elsewhere in a single book.
Author : Jie Li
Publisher :
ISBN 13 :
Total Pages : 326 pages
Book Rating : 4.:/5 (144 download)
Download or read book A Neural Network Optical Character Recognition System written by Jie Li and published by . This book was released on 1990 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Steven C. Elliott
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (268 download)
Download or read book Context Sensitive Optical Character Recognition Using Neural Networks and Hidden Markov Models written by Steven C. Elliott and published by . This book was released on 1992 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This thesis investigates a method for using contextual information in text recognition. This is based on the premise that, while reading, humans recognize words with missing or garbled characters by examining the surrounding characters and then selecting the appropriate character. The correct character is chosen based on an inherent knowledge of the language and spelling techniques. We can then model this statistically. The approach taken by this Thesis is to combine feature extraction techniques, Neural Networks and Hidden Markov Modeling. This method of character recognition involves a three step process: pixel image preprocessing, neural network classification and context interpretation. Pixel image preprocessing applies a feature extraction algorithm to original bit mapped images, which produces a feature vector for the original images which are input into a neural network. The neural network performs the initial classification of the characters by producing ten weights, one for each character. The magnitude of the weight is translated into the confidence the network has in each of the choices. The greater the magnitude and separation, the more confident the neural network is of a given choice. The output of the neural network is the input for a context interpreter. The context interpreter uses Hidden Markov Modeling (HMM) techniques to determine the most probable classification for all characters based on the characters that precede that character and character pair statistics. The HMMs are built using an a priori knowledge of the language: a statistical description of the probabilities of digrams. Experimentation and verification of this method combines the development and use of a preprocessor program, a Cascade Correlation Neural Network and a HMM context interpreter program. Results from these experiments show the neural network successfully classified 88.2 percent of the characters. Expanding this to the word level, 63 percent of the words were correctly identified. Adding the Hidden Markov Modeling improved the word recognition to 82.9 percent."--Abstract.
Author : David T. Au
Publisher :
ISBN 13 :
Total Pages : 206 pages
Book Rating : 4.:/5 (221 download)
Download or read book Optical Character Recognition Using Neural Network written by David T. Au and published by . This book was released on 1993 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Theodor Constantinescu
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (951 download)
Download or read book Optical character recognition using neural networks written by Theodor Constantinescu and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Vijayaraghavan R. Triplicane
Publisher :
ISBN 13 :
Total Pages : 216 pages
Book Rating : 4.:/5 (395 download)
Download or read book Optical Character Recognition from Scene Images by a Neural Network written by Vijayaraghavan R. Triplicane and published by . This book was released on 1995 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Jehad Ibraheem Sahawneh
Publisher :
ISBN 13 :
Total Pages : 336 pages
Book Rating : 4.:/5 (28 download)
Download or read book Optical Character Recognition Using Neural Network Co-processor Board written by Jehad Ibraheem Sahawneh and published by . This book was released on 1993 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Arindam Chaudhuri
Publisher : Springer
ISBN 13 : 3319502522
Total Pages : 260 pages
Book Rating : 4.3/5 (195 download)
Download or read book Optical Character Recognition Systems for Different Languages with Soft Computing written by Arindam Chaudhuri and published by Springer. This book was released on 2016-12-23 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book offers a comprehensive survey of soft-computing models for optical character recognition systems. The various techniques, including fuzzy and rough sets, artificial neural networks and genetic algorithms, are tested using real texts written in different languages, such as English, French, German, Latin, Hindi and Gujrati, which have been extracted by publicly available datasets. The simulation studies, which are reported in details here, show that soft-computing based modeling of OCR systems performs consistently better than traditional models. Mainly intended as state-of-the-art survey for postgraduates and researchers in pattern recognition, optical character recognition and soft computing, this book will be useful for professionals in computer vision and image processing alike, dealing with different issues related to optical character recognition.
Author : Chih-Chin Yang
Publisher :
ISBN 13 :
Total Pages : 156 pages
Book Rating : 4.:/5 (358 download)
Download or read book Optical Character Recognition Using Neural Networks written by Chih-Chin Yang and published by . This book was released on 1994 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Jason Moix
Publisher :
ISBN 13 :
Total Pages : 76 pages
Book Rating : 4.:/5 (122 download)
Download or read book Optical character recognition via neural networks written by Jason Moix and published by . This book was released on 2015 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Horst Bunke
Publisher : World Scientific
ISBN 13 : 9789810222703
Total Pages : 868 pages
Book Rating : 4.2/5 (227 download)
Download or read book Handbook of Character Recognition and Document Image Analysis written by Horst Bunke and published by World Scientific. This book was released on 1997 with total page 868 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optical character recognition and document image analysis have become very important areas with a fast growing number of researchers in the field. This comprehensive handbook with contributions by eminent experts, presents both the theoretical and practical aspects at an introductory level wherever possible.
Author : Chen-Yu Lee
Publisher :
ISBN 13 :
Total Pages : 89 pages
Book Rating : 4.:/5 (951 download)
Download or read book Enhanced Convolutional Neural Networks and Their Application to Photo Optical Character Recognition written by Chen-Yu Lee and published by . This book was released on 2016 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents two principled approaches to improve the performance of convolutional neural networks on visual recognition and demonstrates the effectiveness of CNNs on optical character recognition problem. First, we propose deeply-supervised nets (DSN), a method that simultaneously minimizes classification error and improves the directness and transparency of the hidden layer learning process. We focus our attention on three aspects of traditional CNN-type architectures: (1) transparency in the effect intermediate layers have on overall classification; (2) discriminativeness and robustness of learned features, especially in early layers; (3) training effectiveness in the face of "vanishing" gradients. To combat these issues, we introduce "companion" objective functions at each hidden layer, in addition to the overall objective function at the output layer. Second, we seek to improve deep neural networks by generalizing the pooling operations that play a central role in current architectures. The two primary directions lie in (1) learning a pooling function via combining of max and average pooling, and (2) learning a pooling function in the form of a tree-structured fusion of pooling filters that are themselves learned. In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling. The advantages provided by the proposed methods are evident in our experimental results, showing state-of-the-art performance on MNIST, CIFAR-10, CIFAR-100, and SVHN. Finally, we present recursive recurrent neural networks with attention modeling for lexicon-free optical character recognition in natural scene images. The primary advantages of the proposed method are: (1) use of recursive convolutional neural networks (CNNs), which allow for parametrically efficient and effective image feature extraction; (2) an implicitly learned character-level language model, embodied in a recurrent neural network which avoids the need to use N-grams; and (3) the use of a soft-attention mechanism, allowing the model to selectively exploit image features in a coordinated way, and allowing for end-to-end training within a standard backpropagation framework. We validate our method with state-of-the-art performance on challenging benchmark datasets: Street View Text, IIIT5k, ICDAR and Synth90k.
Author : Cornelius T. Leondes
Publisher : Elsevier
ISBN 13 : 0080551440
Total Pages : 407 pages
Book Rating : 4.0/5 (85 download)
Download or read book Image Processing and Pattern Recognition written by Cornelius T. Leondes and published by Elsevier. This book was released on 1998-02-09 with total page 407 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image Processing and Pattern Recognition covers major applications in the field, including optical character recognition, speech classification, medical imaging, paper currency recognition, classification reliability techniques, and sensor technology. The text emphasizes algorithms and architectures for achieving practical and effective systems, and presents many examples. Practitioners, researchers, and students in computer science, electrical engineering, andradiology, as well as those working at financial institutions, will value this unique and authoritative reference to diverse applications methodologies. Coverage includes: Optical character recognition Speech classification Medical imaging Paper currency recognition Classification reliability techniques Sensor technology Algorithms and architectures for achieving practical and effective systems are emphasized, with many examples illustrating the text. Practitioners, researchers, and students in computer science, electrical engineering, and radiology, as wellk as those working at financial institutions, will find this volume a unique and comprehensive reference source for this diverse applications area.
Author : Vishal Vijayshankar Mishra
Publisher :
ISBN 13 :
Total Pages : 68 pages
Book Rating : 4.:/5 (111 download)
Download or read book Sequence-to-sequence Learning Using Deep Learning for Optical Character Recognition (OCR) written by Vishal Vijayshankar Mishra and published by . This book was released on 2017 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, the deep learning techniques called Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are used to address the problem of Optical Character Recognition (OCR). A special case of RNN called Long Short-Term Memory (LSTM) is used in this research to process the data sequentially. OCR is a process to convert the images containing characters into text. In this research, the images of the mathematical equations from Image-to-Latex 100K data set obtained from OPENAI organization is being used. The mathematical equations from the images are converted into Latex representation using deep learning techniques. The Latex texts were used to again recreate the mathematical equation to test the accuracy of the technique. Unlike previous techniques (Like INFTY) where models were fed with non-tokenized data, the proposed method used the tokenized data to be fed sequentially to the deep learning neural network. The sequential process helps the algorithms to keep track of the processed data and yield high accuracy. In this research, a new variant of LSTM called LSTM with peephole connections and Stochastic Hard Attention model was used. The performance of the proposed deep learning neural network, LSTM with peephole connections and Stochastic Hard Attention model is compared with INFTY (which uses no RNN) and WYGIWYS (which uses RNN). It has been found that the proposed algorithm gives a better accuracy of 76% as compared of 74% achieved by WYGIWYS.
