Chaudhuri, Arindam.
Optical Character Recognition Systems for Different Languages with Soft Computing [electronic resource] / by Arindam Chaudhuri, Krupa Mandaviya, Pratixa Badelia, Soumya K Ghosh. - 1st ed. 2017. - XIX, 248 p. 95 illus. online resource. - Studies in Fuzziness and Soft Computing, 352 1860-0808 ; . - Studies in Fuzziness and Soft Computing, 352 .
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.
9783319502526
10.1007/978-3-319-50252-6 doi
Computational intelligence.
Pattern recognition systems.
Computational linguistics.
Artificial intelligence.
Computational Intelligence.
Automated Pattern Recognition.
Computational Linguistics.
Artificial Intelligence.
Q342
006.3
Optical Character Recognition Systems for Different Languages with Soft Computing [electronic resource] / by Arindam Chaudhuri, Krupa Mandaviya, Pratixa Badelia, Soumya K Ghosh. - 1st ed. 2017. - XIX, 248 p. 95 illus. online resource. - Studies in Fuzziness and Soft Computing, 352 1860-0808 ; . - Studies in Fuzziness and Soft Computing, 352 .
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.
9783319502526
10.1007/978-3-319-50252-6 doi
Computational intelligence.
Pattern recognition systems.
Computational linguistics.
Artificial intelligence.
Computational Intelligence.
Automated Pattern Recognition.
Computational Linguistics.
Artificial Intelligence.
Q342
006.3