Character Recognition Systems: A Guide for Students and Practitioners

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.

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