Character recognition, often referred to as Optical Character Recognition (OCR), is a fascinating field within computer vision that focuses on converting different types of documents, such as scanned paper documents, PDFs, or images captured by a digital camera, into editable and searchable data. For those interested in delving deeper into this topic, several books provide comprehensive insights and practical knowledge.
"Handbook of Character Recognition and Document Image Analysis" by H. Bunke and P. S. P. Wang: This book serves as a valuable resource, offering a detailed exploration of the fundamental concepts and methodologies in character recognition. It covers various techniques used in both machine-printed and handwritten character recognition.
"Optical Character Recognition: An Illustrated Guide" by Stephen V. Rice, George Nagy, and Thomas A. Nartker: This book provides a visual approach to understanding OCR technologies. It includes numerous illustrations and examples, making complex concepts more accessible.
"Document Image Analysis" by Lawrence O'Gorman and Rangachar Kasturi: This text delves into the broader field of document image analysis, with a significant focus on character recognition. It examines the algorithms and techniques used to process and analyze document images.
"Pattern Recognition and Machine Learning" by Christopher M. Bishop: While not exclusively about character recognition, this book provides a solid foundation in pattern recognition and machine learning, both of which are crucial for understanding and developing OCR systems.
"Digital Document Processing: Major Directions and Recent Advances" by Bidyut B. Chaudhuri: This book covers recent advancements in digital document processing, including character recognition, and offers insights into the challenges and solutions in the field.
These books are excellent starting points for anyone looking to enhance their understanding of character recognition and its applications within computer vision systems.