國立虎尾科技大學 |

Optical character recognition systems for different languages with soft computing

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Optical character recognition systems for different languages with soft computing/ by Arindam Chaudhuri ... [et al.].
其他作者:	Chaudhuri, Arindam.
出版者:	Cham :Springer International Publishing : : 2017.,
面頁冊數:	xix, 248 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Optical character recognition. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-50252-6
ISBN:	9783319502526

Optical character recognition systems for different languages with soft computing
Optical character recognition systems for different languages with soft computing[electronic resource] /by Arindam Chaudhuri ... [et al.]. - Cham :Springer International Publishing :2017. - xix, 248 p. :ill., digital ;24 cm. - Studies in fuzziness and soft computing,v.3521434-9922 ;. - Studies in fuzziness and soft computing ;vol. 45..

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.

ISBN: 9783319502526

Standard No.: 10.1007/978-3-319-50252-6doiSubjects--Topical Terms:

883964
Optical character recognition.

LC Class. No.: Q342

Dewey Class. No.: 006.424

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520 $a 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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