Optical Character Recognition Based Retrieval

The automation of manual activities was a long-time imagination of human beings. With the development of computers, this dream is coming to be realized. Over the past 50 years, many researches are conducted to develop machines and software that help and sometimes replace human beings. Optical Character Recognition(OCR) is one of the most successful applications of technology in the field of pattern recognition and artificial intelligence. OCR systems take scanned images of paper documents as input, and automatically convert them into digital format for computer-aided data processing. In the first part of this book, the definition, architecture, benefits, applications and issues of developing of OCR systems are covered. A total of fifteen researches are reviewed and a summary of the identified problems, objective of the study, methods, techniques and algorithms applied or innovated, scope and limitation, performance evaluation as well as the future research direction pointed by all researches are presented in an interesting manner. Finally conclusion and recommendations with references to reviewed literatures are presented.

The amounts of information that are ?ooding people both at the workplace and in private life have increased dramatically in the past ten years. The number of paper documents doubles every four years, and the amount of information stored on all data carriers every six years. New knowledge, however, increases at a considerably lower rate. Possibilities for automatic content recognition in various media and for the processing of documents are therefore becoming more important every day. Especially in economic terms, the e?cient handling of information, i.e., ?- ing the right information at the right time, is an invaluable resource for any enterprise, but it is particularly important for small- and medium-sized ent- prises. The market for document management systems, which in Europe had a volume of approximately 5 billion euros in 2000, will increase considerably over the next few years. The BMBF recognized this development at an early stage. As early as in 1995, it pooled national capabilities in this ?eld in order to support research on the automatic processing of information within the framework of a large collaborative project (READ) involving both industrial companies and research centres. Evaluation of the results led to the conclusion that research work had been successful, and, in a second phase, funding was provided for the colla- rative follow-up project Adaptive READ from 1999 to 2003. The completion of thesetwoimportantlong-termresearchprojectshascontributedsubstantiallyto improving the possibilities of content recognition and processing of handwritten, printed and electronic documents.

This is the first comprehensive text on Optical Character Recognition for Indic scripts. It covers many topics and describes OCR systems for eight different scripts—Bangla, Devanagari, Gurmukhi, Gujarti, Kannada, Malayalam, Tamil and Urdu.

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.

Optical character recognition and document image analysis have become very important areas with a fast growing number of researchers in the field. This comprehensive handbook with contributions by eminent experts, presents both the theoretical and practical aspects at an introductory level wherever possible.

This book constitutes the thoroughly refereed post-workshop-proceedings of the 4th International Workshop on Camera-Based Document Analysis and Recognition, CBDAR 2011, held in Beijing, China, in September 2011. The 13 revised full papers presented were carefully selected during a second round of reviewing and improvement from numerous original submissions. Intended to give a snapshot of the state-of-the-art research in the field of camera based document analysis and recognition, the papers are organized in topical sections on text detection and recognition in scene images, camera-based systems, and datasets and evaluation.

This book brings all the major and frontier topics in the field of document analysis together into a single volume, creating a unique reference source that will be invaluable to a large audience of researchers, lecturers and students working in this field. With chapters written by some of the most distinguished researchers active in this field, this book addresses recent advances in digital document processing research and development.

Pattern recognition basically deals with the recognition of patterns, shapes, objects, things in images. Document image analysis was one of the very ?rst applications of pattern recognition and even of computing. But until the 1980s, research in this ?eld was mainly dealing with text-based documents, including OCR (Optical Character Recognition) and page layout analysis. Only a few people were looking at more speci?c documents such as music sheet, bank cheques or forms. The community of graphics recognition became visible in the late 1980s. Their speci?c interest was to recognize high-level objects represented by line drawings and graphics. The speci?c pattern recognition problems they had to deal with was raster-to-graphics conversion (i.e., recognizing graphical primitives in a cluttered pixel image), text-graphics separation, and symbol recognition. The speci?c problem of symbol recognition in graphical documents has received a lot of attention. The symbols to be recognized can be musical notation, electrical symbols, architectural objects, pictograms in maps, etc. At ?rst glance, the symbol recognition problems seems to be very similar to that of character recognition; - ter all, characters are basically a subset of symbols. Therefore, the large know-how in OCR has been extensively used in graphical symbol recognition: starting with segmenting the document to extract the symbols, extracting features from the s- bols, and then recognizing them through classi?cation or matching, with respect to a training/learning set.

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.