Optical Character Recognition Systems for Different Languages with Soft Computing [electronic resource] / by Arindam Chaudhuri, Krupa Mandaviya, Pratixa Badelia, Soumya K Ghosh.
By: Chaudhuri, Arindam [author.]
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Contributor(s): Mandaviya, Krupa [author.] | Badelia, Pratixa [author.] | K Ghosh, Soumya [author.] | SpringerLink (Online service).
Material type: 
BookSeries: Studies in Fuzziness and Soft Computing: 352Publisher: Cham : Springer International Publishing : Imprint: Springer, 2017Edition: 1st ed. 2017.Description: XIX, 248 p. 95 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319502526.Subject(s): Computational intelligence | Pattern recognition systems | Computational linguistics | Artificial intelligence | Computational Intelligence | Automated Pattern Recognition | Computational Linguistics | Artificial IntelligenceAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
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Springer Nature eBookSummary: 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.
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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