Handwritten Bangla Digit Recognition Using Classifier Combination Through DS Technique
Identifieur interne : 001296 ( Main/Exploration ); précédent : 001295; suivant : 001297Handwritten Bangla Digit Recognition Using Classifier Combination Through DS Technique
Auteurs : Subhadip Basu [Inde] ; Ram Sarkar [Inde] ; Nibaran Das [Inde] ; Mahantapas Kundu [Inde] ; Mita Nasipuri [Inde] ; Kumar Basu [Inde]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.
Descripteurs français
- Pascal (Inist)
English descriptors
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Abstract
Abstract: The work presents an application of Dempster-Shafer (DS) technique for combination of classification decisions obtained from two Multi Layer Perceptron (MLP) based classifiers for optical character recognition (OCR) of handwritten Bangla digits using two different feature sets. Bangla is the second most popular script in the Indian subcontinent and the fifth most popular language in the world. The two feature sets used for the work are so designed that they can supply complementary information, at least to some extent, about the classes of digit patterns to the MLP classifiers. On experimentation with a database of 6000 samples, the technique is found to improve recognition performances by a minimum of 1.2% and a maximum of 2.32% compared to the average recognition rate of the individual MLP classifiers after 3-fold cross validation of results. The overall recognition rate as observed for the same is 95.1% on average.
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DOI: 10.1007/11590316_32
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Dept., Jadavpur University, 700032, Kolkata</wicri:regionArea><wicri:noRegion>Kolkata</wicri:noRegion></affiliation></author><author><name sortKey="Kundu, Mahantapas" sort="Kundu, Mahantapas" uniqKey="Kundu M" first="Mahantapas" last="Kundu">Mahantapas Kundu</name><affiliation wicri:level="1"><country xml:lang="fr">Inde</country><wicri:regionArea>Computer Sc. & Engg. Dept., Jadavpur University, 700032, Kolkata</wicri:regionArea><wicri:noRegion>Kolkata</wicri:noRegion></affiliation></author><author><name sortKey="Nasipuri, Mita" sort="Nasipuri, Mita" uniqKey="Nasipuri M" first="Mita" last="Nasipuri">Mita Nasipuri</name><affiliation wicri:level="1"><country xml:lang="fr">Inde</country><wicri:regionArea>Computer Sc. & Engg. Dept., Jadavpur University, 700032, Kolkata</wicri:regionArea><wicri:noRegion>Kolkata</wicri:noRegion></affiliation></author><author><name sortKey="Basu, Kumar" sort="Basu, Kumar" uniqKey="Basu K" first="Kumar" last="Basu">Kumar Basu</name><affiliation wicri:level="1"><country xml:lang="fr">Inde</country><wicri:regionArea>Computer Sc. & Engg. Dept., Jadavpur University, 700032, Kolkata</wicri:regionArea><wicri:noRegion>Kolkata</wicri:noRegion></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2005</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">520B095D23E6AD19CA60F6695234FCE19227BDBD</idno><idno type="DOI">10.1007/11590316_32</idno><idno type="ChapterID">32</idno><idno type="ChapterID">Chap32</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Character recognition</term><term>Cross validation</term><term>Handwritten character recognition</term><term>Multilayer perceptrons</term><term>Neural network</term><term>Optical character recognition</term><term>Pattern recognition</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Handwritten bangla digit recognition</term><term>Perceptron multicouche</term><term>Reconnaissance caractère</term><term>Reconnaissance caractère manuscrit</term><term>Reconnaissance forme</term><term>Reconnaissance optique caractère</term><term>Réseau neuronal</term><term>Validation croisée</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: The work presents an application of Dempster-Shafer (DS) technique for combination of classification decisions obtained from two Multi Layer Perceptron (MLP) based classifiers for optical character recognition (OCR) of handwritten Bangla digits using two different feature sets. Bangla is the second most popular script in the Indian subcontinent and the fifth most popular language in the world. The two feature sets used for the work are so designed that they can supply complementary information, at least to some extent, about the classes of digit patterns to the MLP classifiers. On experimentation with a database of 6000 samples, the technique is found to improve recognition performances by a minimum of 1.2% and a maximum of 2.32% compared to the average recognition rate of the individual MLP classifiers after 3-fold cross validation of results. The overall recognition rate as observed for the same is 95.1% on average.</div></front></TEI><affiliations><list><country><li>Inde</li></country></list><tree><country name="Inde"><noRegion><name sortKey="Basu, Subhadip" sort="Basu, Subhadip" uniqKey="Basu S" first="Subhadip" last="Basu">Subhadip Basu</name></noRegion><name sortKey="Basu, Kumar" sort="Basu, Kumar" uniqKey="Basu K" first="Kumar" last="Basu">Kumar Basu</name><name sortKey="Das, Nibaran" sort="Das, Nibaran" uniqKey="Das N" first="Nibaran" last="Das">Nibaran Das</name><name sortKey="Kundu, Mahantapas" sort="Kundu, Mahantapas" uniqKey="Kundu M" first="Mahantapas" last="Kundu">Mahantapas Kundu</name><name sortKey="Nasipuri, Mita" sort="Nasipuri, Mita" uniqKey="Nasipuri M" first="Mita" last="Nasipuri">Mita Nasipuri</name><name sortKey="Sarkar, Ram" sort="Sarkar, Ram" uniqKey="Sarkar R" first="Ram" last="Sarkar">Ram Sarkar</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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