Optical Character Recognition Algorithms And Systems
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Optical Character Recognition Algorithms and Systems
"Optical Character Recognition Algorithms and Systems" Designed for researchers, engineers, and advanced practitioners, "Optical Character Recognition Algorithms and Systems" offers a comprehensive exploration of the theory, algorithms, and architectures underpinning modern OCR technology. The book begins by grounding the reader in the mathematical formalism and theoretical foundations essential for understanding character recognition, tracing the historical evolution from early optical readers to artificial intelligence-powered systems. It presents a thorough taxonomy of OCR engines and delineates the rigorous benchmarking protocols required to assess advancement in this field. Covering the entire OCR pipeline, the text delves deeply into document image acquisition, preprocessing, and segmentation—addressing challenges posed by varied input devices, physical and digital noise, and complex page layouts. Subsequent chapters explore sophisticated feature extraction techniques, from classical handcrafted features to state-of-the-art deep learning representations, and provide an authoritative treatment of recognition models, including statistical classifiers, kernel methods, and advanced neural architectures such as attention mechanisms and transformers. The book pays particular attention to multilingual and handwritten text recognition, describing domain-specific algorithms, script detection strategies, and the unique obstacles presented by cursive and overlapping characters. Rounding out its approach, the book analyzes essential postprocessing techniques—including error correction, language modeling, and active learning—as well as practical considerations for deploying OCR at scale. Readers will find in-depth discussions on modular and real-time architectures, integration with enterprise workflows, and security and privacy in distributed systems. The concluding chapters highlight the latest advancements, from scene and video text recognition to augmented reality applications, and outline open research challenges, making this volume an indispensable reference for anyone advancing the state of document analysis and recognition.
Optical Character Recognition Systems for Different Languages with Soft Computing
The book offers a comprehensive survey of soft-computing models for optical character recognition systems. The various techniques, including fuzzy and rough sets, artificial neural networks and genetic algorithms, are tested using real texts written in different languages, such as English, French, German, Latin, Hindi and Gujrati, which have been extracted by publicly available datasets. The simulation studies, which are reported in details here, show that soft-computing based modeling of OCR systems performs consistently better than traditional models. Mainly intended as state-of-the-art survey for postgraduates and researchers in pattern recognition, optical character recognition and soft computing, this book will be useful for professionals in computer vision and image processing alike, dealing with different issues related to optical character recognition.
Optical Character Recognition
Author: Stephen V. Rice
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Optical character recognition (OCR) is the most prominent and successful example of pattern recognition to date. There are thousands of research papers and dozens of OCR products. Optical Character Rcognition: An Illustrated Guide to the Frontier offers a perspective on the performance of current OCR systems by illustrating and explaining actual OCR errors. The pictures and analysis provide insight into the strengths and weaknesses of current OCR systems, and a road map to future progress. Optical Character Recognition: An Illustrated Guide to the Frontier will pique the interest of users and developers of OCR products and desktop scanners, as well as teachers and students of pattern recognition, artificial intelligence, and information retrieval. The first chapter compares the character recognition abilities of humans and computers. The next four chapters present 280 illustrated examples of recognition errors, in a taxonomy consisting of Imaging Defects, Similar Symbols, Punctuation, and Typography. These examples were drawn from large-scale tests conducted by the authors. The final chapter discusses possible approaches for improving the accuracy of today's systems, and is followed by an annotated bibliography. Optical Character Recognition: An Illustrated Guide to the Frontier is suitable as a secondary text for a graduate level course on pattern recognition, artificial intelligence, and information retrieval, and as a reference for researchers and practitioners in industry.