OcrV1, Istex, Corpus, bibRecord, 001318

Arabic Handwritten Characters Classification Using Learning Vector Quantization Algorithm

Identifieur interne : 001318 ( Istex/Corpus ); précédent : 001317; suivant : 001319

Arabic Handwritten Characters Classification Using Learning Vector Quantization Algorithm

Auteurs : A. Ali

Source :

Lecture Notes in Computer Science [ 0302-9743 ] ; 2008.

RBID : ISTEX:434463313ECD1F266E3550CA1C07CCD619C49AAB

Abstract

Abstract: In this module, Learning Vector Quantization LVQ neural network is first time introduced as a classifier for Arabic handwritten character. Classification has been performed in two different strategies, in first strategy, we use one classifier for all 53 Arabic Character Basic Shapes CBSs in training and testing phases, in second strategy we use three classifiers for three subsets of 53 Arabic CBSs, the three subsets of Arabic CBSs are; ascending CBSs, descending CBSs and embedded CBSs. Three training algorithms; OLVQ1, LVQ2 and LVQ3 were examined and OLVQ1 found as the best learning algorithm.

Url:

https://api.istex.fr/document/434463313ECD1F266E3550CA1C07CCD619C49AAB/fulltext/pdf

DOI: 10.1007/978-3-540-69905-7_53

Links to Exploration step

ISTEX:434463313ECD1F266E3550CA1C07CCD619C49AAB

Le document en format XML

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<ChapterTitle Language="En">Arabic Handwritten Characters Classification Using Learning Vector Quantization Algorithm</ChapterTitle>
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<ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Mohamed</GivenName>
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<Affiliation ID="Aff20"><OrgDivision>Computer Science Dept., Faculty of Science</OrgDivision>
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<Para>In this module, Learning Vector Quantization LVQ neural network is first time introduced as a classifier for Arabic handwritten character. Classification has been performed in two different strategies, in first strategy, we use one classifier for all 53 Arabic Character Basic Shapes CBSs in training and testing phases, in second strategy we use three classifiers for three subsets of 53 Arabic CBSs, the three subsets of Arabic CBSs are; ascending CBSs, descending CBSs and embedded CBSs. Three training algorithms; OLVQ1, LVQ2 and LVQ3 were examined and OLVQ1 found as the best learning algorithm.</Para>
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<KeywordGroup Language="En"><Heading>Keywords</Heading>
<Keyword>Arabic handwritten recognition</Keyword>
<Keyword>Neural Network</Keyword>
<Keyword>Classification</Keyword>
<Keyword>Character Recognition</Keyword>
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<abstract lang="en">Abstract: In this module, Learning Vector Quantization LVQ neural network is first time introduced as a classifier for Arabic handwritten character. Classification has been performed in two different strategies, in first strategy, we use one classifier for all 53 Arabic Character Basic Shapes CBSs in training and testing phases, in second strategy we use three classifiers for three subsets of 53 Arabic CBSs, the three subsets of Arabic CBSs are; ascending CBSs, descending CBSs and embedded CBSs. Three training algorithms; OLVQ1, LVQ2 and LVQ3 were examined and OLVQ1 found as the best learning algorithm.</abstract>
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<namePart type="family">Elmoataz</namePart>
<affiliation>GREYC UMR CNRS 6072 ENSICAEN, Université de Caen Basse-Normandie, 6 Bd. Maréchal Juin, 14050, Caen, France</affiliation>
<affiliation>E-mail: abderrahim.elmoataz-billah@unicaen.fr</affiliation>
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<name type="personal"><namePart type="given">Olivier</namePart>
<namePart type="family">Lezoray</namePart>
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<name type="personal"><namePart type="given">Fathallah</namePart>
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<name type="personal"><namePart type="given">Driss</namePart>
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<biblStruct xml:id="b6"><analytic><title level="a" type="main">Shape-Based Alphabet for Off-line Arabic Handwriting Recognition</title>
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</biblStruct>
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Arabic Handwritten Characters Classification Using Learning Vector Quantization Algorithm

Arabic Handwritten Characters Classification Using Learning Vector Quantization Algorithm

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