Handbook Of Character Recognition And Document Image Analysis

Author: Horst Bunke
Publisher: World Scientific
ISBN: 9789810222703
Size: 43.54 MB
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Handbook Of Character Recognition And Document Image Analysis from the Author: Horst Bunke. 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.

Handbook Of Document Image Processing And Recognition

Author: David Doermann
Publisher: Springer
ISBN: 9780857298584
Size: 63.11 MB
Format: PDF, ePub
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Handbook Of Document Image Processing And Recognition from the Author: David Doermann. The Handbook of Document Image Processing and Recognition is a comprehensive resource on the latest methods and techniques in document image processing and recognition. Each chapter provides a clear overview of the topic followed by the state of the art of techniques used – including elements of comparison between them – along with supporting references to archival publications, for those interested in delving deeper into topics addressed. Rather than favor a particular approach, the text enables the reader to make an informed decision for their specific problems.

Machine Learning In Document Analysis And Recognition

Author: Simone Marinai
Publisher: Springer Science & Business Media
ISBN: 3540762795
Size: 50.57 MB
Format: PDF, ePub
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Machine Learning In Document Analysis And Recognition from the Author: Simone Marinai. The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphicalcomponents of a document and to extract information. With ?rst papers dating back to the 1960’s, DAR is a mature but still gr- ing research?eld with consolidated and known techniques. Optical Character Recognition (OCR) engines are some of the most widely recognized pr- ucts of the research in this ?eld, while broader DAR techniques are nowadays studied and applied to other industrial and o?ce automation systems. In the machine learning community, one of the most widely known - search problems addressed in DAR is recognition of unconstrained handwr- ten characters which has been frequently used in the past as a benchmark for evaluating machine learning algorithms, especially supervised classi?ers. However, developing a DAR system is a complex engineering task that involves the integration of multiple techniques into an organic framework. A reader may feel that the use of machine learning algorithms is not approp- ate for other DAR tasks than character recognition. On the contrary, such algorithms have been massively used for nearly all the tasks in DAR. With large emphasis being devoted to character recognition and word recognition, other tasks such as pre-processing, layout analysis, character segmentation, and signature veri?cation have also bene?ted much from machine learning algorithms.

Character Recognition Systems

Author: Mohamed Cheriet
Publisher: John Wiley & Sons
ISBN: 9780470176528
Size: 43.77 MB
Format: PDF, Mobi
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Character Recognition Systems from the Author: Mohamed Cheriet. "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.

Handbook Of Pattern Recognition And Computer Vision

Author: C H Chen
Publisher: World Scientific
ISBN: 9814656542
Size: 47.57 MB
Format: PDF, ePub, Docs
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Handbook Of Pattern Recognition And Computer Vision from the Author: C H Chen. Pattern recognition, image processing and computer vision are closely linked areas which have seen enormous progress in the last fifty years. Their applications in our daily life, commerce and industry are growing even more rapidly than theoretical advances. Hence, the need for a new handbook in pattern recognition and computer vision every five or six years as envisioned in 1990 is fully justified and valid. The book consists of three parts: (1) Pattern recognition methods and applications; (2) Computer vision and image processing; and (3) Systems, architecture and technology. This book is intended to capture the major developments in pattern recognition and computer vision though it is impossible to cover all topics. The chapters are written by experts from many countries, fully reflecting the strong international research interests in the areas. This fifth edition will complement the previous four editions of the book. Contents:Pattern Recognition Methods and Applications:Syntactic Pattern Recognition: Paradigm Issues and Open Problems (Mariusz Flasiński)Deep Discriminative and Generative Models for Speech Pattern Recognition (Li Deng and Navdeep Jaitly)On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and An Example in Semi-supervised Learning (Marco Loog, Jesse H Krijthe, and Are C Jensen)Information Theoretic Clustering Using a k-Nearest Neighbors-based Divergence Measure (Vidar V Vikjord and Robert Jenssen)Pruning Trees in Random Forests for Minimizing Non Detection in Medical Imaging (Laurent Heutte, Caroline Petitjean, and Chesner Désir)Recent Advances on Optimum-path Forest for Data Classification: Supervised, Semi-supervised, and Unsupervised Learning (João Paulo Papa, Willian Paraguassu Amorim, Alexandre Xavier Falcão, and João Manuel R S Tavares)On Curvelet-based Texture Features for Pattern Classification (Ching-Chung Li and Wen-Chyi Lin)Computer Recognition and Evaluation of Coins (Bo-Yuan Feng, Ke Sun, Parmida Atighechian, and Ching Y Suen)Supervised and Unsupervised Feature Descriptors for Error-resilient Underwater Live Fish Recognition (Meng-Che Chuang, Jeng-Neng Hwang, and Kresimir Willimans)Model Adaptation for Personalized Music Emotion Recognition (Yi-Hsuan Yang, Ju-Chiang Wang, Yu-An Chen, and Homer H Chen)Computer Vision and Image Processing:Context Assisted Person Identification for Images and Videos (Liyan Zhang, Dmitri V Kalashnikov, and Sharad Mehrotra)Statistical Shape Spaces for 3D Data: A Review (Alan Brunton, Augusto Salazar, Timo Bolkart, and Stefanie Wuhrer)Tracking Without Appearance Descriptors (Mehrsan Javan Roshtkari and Martin D Levine)Knowledge Augmented Visual Learning (Ziheng Wang and Qiang Ji)Graph Edit Distance — Novel Approximation Algorithms (Kaspar Riesen and Horst Bunke)Latest Developments of LSTM Neural Networks with Applications of Document Image Analysis (Marcus Liwicki, Volkmar Frinken, and Muhammad Zeshan Afzal)Analyzing Remote Sensing Images with Hierarchial Morphological Representations (Gabriele Cavallaro, Mauro Dalla Mura and Jón Atli Benediktsson)Manifold-Based Sparse Representation for Hyperspectral Image Classification (Yuan Yan Tang and Haoliang Yuan)A Review of Texture Classification Methods and Their Applications in Medical Image Analysis of the Brain (Rouzbeh Maani, Sanjay Kalra, and Yee-Hong Yang)3D Tomosynthesis to Detect Breast Cancer (Yanbin Lu, Mina Yousefi, John Ellenberger, Richard H Moore, Daniel B Kopans, Adam Krzyżak, and Ching Y Suen)System, Architecture, and Technology:Combining Representations for Improved Sketch Recognition (Sonya Cates)Visual Object Recognition with Image Retrieval (Sedat Ozer)Efficient Identification of Faces in Video Streams Using Low-power Multi-core Devices (Donavan Prieur, Eric Granger, Yvon Savaria, and Claude Thibeault)Kernel-based Learning for Fault Detection and Identification in Fuel Cell Systems (Gabriele Moser, Paola Costamagna, Andrea De Giorgi, Lissy Pellaco, Andrea Trucco, and Sebastiano B Serpico)Outdoor Shadow Modelling and its Applications (Lin Gu and Antonio Robles-Kelly)Fast Structured Tracker with Improved Motion Model Using Robust Kalman Filter (Ivan Bogun and Eraldo Ribeiro)Using 3D Vision for Automated Industrial Inspection (David J Michael)Vision Challenges in Image-based Barcode Readers (Xianju Wang and Xiangyun (Mary) Ye)Parallel Pattern Matching Using the Automata Processor (Matt Tanner, Matt Grimm, and Harold B Noyes) Readership: Graduate students, academics, practitioners, researchers, computer scientists, electrical and medical engineers.Key Features:This book provides a comprehensive and concise account of major developments in pattern recognition and is written by leading experts in the fieldsThis book provides coverage of major applications in human identification, medical imaging, remote sensing, speech/ audio processing, and industrial machine visionThis book provides in-depth coverage of fundamental theory of pattern recognition and computer visionKeywords:Pattern Recognition/Image Analysis;Machine Perception/Computer Vision;Speech/Audio Recognition;Personal Identification;Remote Sensing Recognition;Industrial Vision;Medical Imaging and Recognition

Handbook Of Natural Language Processing

Author: Robert Dale
Publisher: CRC Press
ISBN: 0824746341
Size: 34.58 MB
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Handbook Of Natural Language Processing from the Author: Robert Dale. This study explores the design and application of natural language text-based processing systems, based on generative linguistics, empirical copus analysis, and artificial neural networks. It emphasizes the practical tools to accommodate the selected system.

Data Mining Concepts And Techniques

Author: Jiawei Han
Publisher: Elsevier
ISBN: 9780123814807
Size: 48.92 MB
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Data Mining Concepts And Techniques from the Author: Jiawei Han. Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

Handbook Of Research On Machine Learning Innovations And Trends

Author: Aboul Ella Hassanien
ISBN: 9781522522294
Size: 68.13 MB
Format: PDF
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Handbook Of Research On Machine Learning Innovations And Trends from the Author: Aboul Ella Hassanien. Continuous improvements in technological applications have allowed more opportunities to develop automated systems. This not only leads to higher success in smart data analysis, but it increases the overall probability of technological progression. The Handbook of Research on Machine Learning Innovations and Trends is a key resource on the latest advances and research regarding the vast range of advanced systems and applications involved in machine intelligence. Highlighting multidisciplinary studies on decision theory, intelligent search, and multi-agent systems, this publication is an ideal reference source for professionals and researchers working in the field of machine learning and its applications.

Image Processing In C

Author: Dwayne Phillips
Publisher: Prentice Hall
ISBN: 9780131045484
Size: 50.61 MB
Format: PDF, Mobi
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Image Processing In C from the Author: Dwayne Phillips. Aiming to teach the fundamentals of image processing, this text provides image processing tools and includes processing software. It explains basic concepts, implements the operations in C, and provides ready-to-use working edge detectors, filters and histogram equalizers.