Thinning is a technique widely used in the pre-processing stage of a pattern recognition system to compress data and to enhance feature extraction in the subsequent stage. It reduces a digitized pattern to a skeleton so that all resulting branches are 1 pixel thick. The method seems easy at first and has many advantages, however after two decades of intensive research, it has been found to be very challenging due to the difficulties in programming computers to do it.This collection of 15 papers by leading scientists working in the area examines the theoretical and experimental aspects of thinning methodologies. The authors have addressed the problems faced, compared their performance results with others, and assessed the challenges ahead. Researchers will find the volume helpful in shedding light on difficult issues and stimulating further research in the area.
This is an up-to-date volume of selected and expanded papers originating from Vision Interface 88, a conference held in Edmonton, Canada. A broad range of topics are covered-from image processing to hardware design. They include robot vision, biomedical imaging, remote sensing and parallel processing, shape recognition and features, computational methods in vision, and three dimensional vision and application.
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.
This book is concerned with linear and nonlinear transformations of digitized images and patterns. Transformation models include linear, quadratic, cubic, bilinear, biquadratic, bicubic, Coons model and other nonlinear forms such as harmonic, projective, and perspective transformations. Discrete techniques have been developed to realize both forward and inverse transformations. The latter can be applied to normalize distorted images and to enhance the pattern recognition process. Efficient algorithms such as the splitting-shooting methods and splitting-integrating methods have been developed and analysed in this book for the first time. Graphical examples are given and compared with existing algorithms. This book is of interest to researchers in the areas of pattern recognition, character recognition, image processing, computer vision, computer graphics and other related fields.
Following the rapid growth of powerful computer hardware and sophisticated software, Chinese computing and data processing has become a very exciting field. This book presents 6,321 of the most commonly used Chinese words in two orderings: one in descending frequency, and the other in phonetic groups according to Suen's new phonetic system. Extracted from a corpus of over forty thousand Chinese words, these most frequently used words cover 90% of almost one million word samples selected from various sources like popular newspapers, novels and various reading materials.The data presented in this book are of the most fundamental importance in the design and construction of word processors and computers for processing Chinese text and data, investigation of lexicographical and phonological matters. Linguists, computer scientists, psychologists, students, teachers, and specialists of the Chinese language, as well as sinologists and scholars in computational studies of Chinese words and sounds should find this book very informative and interesting.
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