Gujarati handwritten numeral optical character reorganization through neural network
Identifieur interne : 000779 ( Main/Exploration ); précédent : 000778; suivant : 000780Gujarati handwritten numeral optical character reorganization through neural network
Auteurs : Apurva A. Desai [Inde]Source :
- Pattern recognition [ 0031-3203 ] ; 2010.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
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:
Links toward previous steps (curation, corpus...)
- to stream PascalFrancis, to step Corpus: 000186
- to stream PascalFrancis, to step Curation: 000591
- to stream PascalFrancis, to step Checkpoint: 000153
- to stream Main, to step Merge: 000784
- to stream Main, to step Curation: 000779
Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" level="a">Gujarati handwritten numeral optical character reorganization through neural network</title>
<author><name sortKey="Desai, Apurva A" sort="Desai, Apurva A" uniqKey="Desai A" first="Apurva A." last="Desai">Apurva A. Desai</name>
<affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Veer Narmad South Gujarat University</s1>
<s2>Surat, Gujarat</s2>
<s3>IND</s3>
<sZ>1 aut.</sZ>
</inist:fA14>
<country>Inde</country>
<wicri:noRegion>Veer Narmad South Gujarat University</wicri:noRegion>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">INIST</idno>
<idno type="inist">10-0212512</idno>
<date when="2010">2010</date>
<idno type="stanalyst">PASCAL 10-0212512 INIST</idno>
<idno type="RBID">Pascal:10-0212512</idno>
<idno type="wicri:Area/PascalFrancis/Corpus">000186</idno>
<idno type="wicri:Area/PascalFrancis/Curation">000591</idno>
<idno type="wicri:Area/PascalFrancis/Checkpoint">000153</idno>
<idno type="wicri:doubleKey">0031-3203:2010:Desai A:gujarati:handwritten:numeral</idno>
<idno type="wicri:Area/Main/Merge">000784</idno>
<idno type="wicri:Area/Main/Curation">000779</idno>
<idno type="wicri:Area/Main/Exploration">000779</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Gujarati handwritten numeral optical character reorganization through neural network</title>
<author><name sortKey="Desai, Apurva A" sort="Desai, Apurva A" uniqKey="Desai A" first="Apurva A." last="Desai">Apurva A. Desai</name>
<affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Veer Narmad South Gujarat University</s1>
<s2>Surat, Gujarat</s2>
<s3>IND</s3>
<sZ>1 aut.</sZ>
</inist:fA14>
<country>Inde</country>
<wicri:noRegion>Veer Narmad South Gujarat University</wicri:noRegion>
</affiliation>
</author>
</analytic>
<series><title level="j" type="main">Pattern recognition</title>
<title level="j" type="abbreviated">Pattern recogn.</title>
<idno type="ISSN">0031-3203</idno>
<imprint><date when="2010">2010</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
<seriesStmt><title level="j" type="main">Pattern recognition</title>
<title level="j" type="abbreviated">Pattern recogn.</title>
<idno type="ISSN">0031-3203</idno>
</seriesStmt>
</fileDesc>
<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Asymmetry</term>
<term>Feedforward neural nets</term>
<term>Manuscript character</term>
<term>Neural network</term>
<term>Optical character recognition</term>
<term>Pattern recognition</term>
</keywords>
<keywords scheme="Pascal" xml:lang="fr"><term>Caractère manuscrit</term>
<term>Réseau neuronal</term>
<term>Reconnaissance optique caractère</term>
<term>Réseau neuronal non bouclé</term>
<term>Asymétrie</term>
<term>Reconnaissance forme</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front><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>
</front>
</TEI>
<affiliations><list><country><li>Inde</li>
</country>
</list>
<tree><country name="Inde"><noRegion><name sortKey="Desai, Apurva A" sort="Desai, Apurva A" uniqKey="Desai A" first="Apurva A." last="Desai">Apurva A. Desai</name>
</noRegion>
</country>
</tree>
</affiliations>
</record>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Ticri/CIDE/explor/OcrV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000779 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 000779 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{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 }}
This area was generated with Dilib version V0.6.32. |