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Gujarati handwritten numeral optical character reorganization through neural network

Identifieur interne : 000779 ( Main/Exploration ); précédent : 000778; suivant : 000780

Gujarati handwritten numeral optical character reorganization through neural network

Auteurs : Apurva A. Desai [Inde]

Source :

RBID : Pascal:10-0212512

Descripteurs français

English descriptors

Abstract

This paper deals with an optical character recognition (OCR) system for handwritten Gujarati numbers. One may find so much of work for Indian languages like Hindi, Kannada, Tamil, Bangala, Malayalam, Gurumukhi etc, but Gujarati is a language for which hardly any work is traceable especially for handwritten characters. Here in this work a neural network is proposed for Gujarati handwritten digits identification. A multi layered feed forward neural network is suggested for classification of digits. The features of Gujarati digits are abstracted by four different profiles of digits. Thinning and skew-correction are also done for preprocessing of handwritten numerals before their classification. This work has achieved approximately 82% of success rate for Gujarati handwritten digit identification.


Affiliations:


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Le document en format XML

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<s1>Veer Narmad South Gujarat University</s1>
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<term>Caractère manuscrit</term>
<term>Réseau neuronal</term>
<term>Reconnaissance optique caractère</term>
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<div type="abstract" xml:lang="en">This paper deals with an optical character recognition (OCR) system for handwritten Gujarati numbers. One may find so much of work for Indian languages like Hindi, Kannada, Tamil, Bangala, Malayalam, Gurumukhi etc, but Gujarati is a language for which hardly any work is traceable especially for handwritten characters. Here in this work a neural network is proposed for Gujarati handwritten digits identification. A multi layered feed forward neural network is suggested for classification of digits. The features of Gujarati digits are abstracted by four different profiles of digits. Thinning and skew-correction are also done for preprocessing of handwritten numerals before their classification. This work has achieved approximately 82% of success rate for Gujarati handwritten digit identification.</div>
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{{Explor lien
   |wiki=    Ticri/CIDE
   |area=    OcrV1
   |flux=    Main
   |étape=   Exploration
   |type=    RBID
   |clé=     Pascal:10-0212512
   |texte=   Gujarati handwritten numeral optical character reorganization through neural network
}}

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