Издательство John Wiley, 2007, -361 pp.In modern society, we rely heavily on computers to process huge volumes of data. Related to this and for economic reasons or business requirements, there is a great demand for quickly inputting the mountains of printed and handwritten information into the computer. Often these data exist on paper and they have to be typed into the computer by human operators, for example, billions of letters in the mail, checks, payment slips, income tax forms, and many other business forms and documents. Such time-consuming and error-prone processes have been lightened by the invention of OCR (optical character recognition) systems that read written data by recognizing them at high speeds, one character at a time. However, the capabilities of the current OCR systems are still quite limited and only a small fraction of the above data are entered into the computer by them. Hence, much effort is still needed to enable them to read printed and handwritten characters more accurately. This book looks at some of the problems involved and describes in depth, some possible solutions, and the latest techniques achieved by researchers in the character recognition field. Although several OCR books have been published before,1 none of them are up-to-date now. In view of this, we have gathered together to write this book. It is our objective to provide the readers with the fundamental principles and state-of-the-art computational methods of reading printed texts and handwritten materials. We have also highlighted the key elements of character recognition2 with extensive illustrations and references so that the readers can pursue further research and studies. It is hoped that this book will help graduate students, professors, researchers, and practitioners to master the theory and latest methodologies in character recognition, and to apply them in their research and practice.Introduction: Character Recognition, Evolution, and Development Tools for Image Preprocessing Feature Extraction, Selection, and Creation Pattern Classification Methods Word and String Recognition Case Studies
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