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.

Describes novel soft-computing methods developed by the authors Highlights the advantages of soft computing models for optical character recognition (OCR) systems in comparison to traditional methods Includes various case studies involving different languages Includes supplementary material: sn.pub/extras

Inhalt

1 Introduction.-2 Optical Character Recognition Systems.-3 Soft Computing Techniques for Optical Character Recognition Systems.-4 Optical Character Recognition Systems for English language.-5 Optical Character Recognition Systems for French language.-6 Optical Character Recognition Systems for German language.-7 Optical Character Recognition Systems for Latin language.-8 Optical Character Recognition Systems for Hindi language.-9 Optical Character Recognition Systems for Gujrati language.-10 Summary and Future Research.-Index.