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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, G...

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Detalles Bibliográficos
Autores principales: Chaudhuri, Arindam, Mandaviya, Krupa, Badelia, Pratixa, K Ghosh, Soumya
Lenguaje:eng
Publicado: Springer 2017
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-50252-6
http://cds.cern.ch/record/2240507
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author Chaudhuri, Arindam
Mandaviya, Krupa
Badelia, Pratixa
K Ghosh, Soumya
author_facet Chaudhuri, Arindam
Mandaviya, Krupa
Badelia, Pratixa
K Ghosh, Soumya
author_sort Chaudhuri, Arindam
collection CERN
description 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.
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institution Organización Europea para la Investigación Nuclear
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publishDate 2017
publisher Springer
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spelling cern-22405072021-04-21T19:23:54Zdoi:10.1007/978-3-319-50252-6http://cds.cern.ch/record/2240507engChaudhuri, ArindamMandaviya, KrupaBadelia, PratixaK Ghosh, SoumyaOptical character recognition systems for different languages with soft computingEngineeringThe 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.Springeroai:cds.cern.ch:22405072017
spellingShingle Engineering
Chaudhuri, Arindam
Mandaviya, Krupa
Badelia, Pratixa
K Ghosh, Soumya
Optical character recognition systems for different languages with soft computing
title Optical character recognition systems for different languages with soft computing
title_full Optical character recognition systems for different languages with soft computing
title_fullStr Optical character recognition systems for different languages with soft computing
title_full_unstemmed Optical character recognition systems for different languages with soft computing
title_short Optical character recognition systems for different languages with soft computing
title_sort optical character recognition systems for different languages with soft computing
topic Engineering
url https://dx.doi.org/10.1007/978-3-319-50252-6
http://cds.cern.ch/record/2240507
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AT mandaviyakrupa opticalcharacterrecognitionsystemsfordifferentlanguageswithsoftcomputing
AT badeliapratixa opticalcharacterrecognitionsystemsfordifferentlanguageswithsoftcomputing
AT kghoshsoumya opticalcharacterrecognitionsystemsfordifferentlanguageswithsoftcomputing