Active Handwritten Character Recognition Using Genetic Programming
Identifieur interne : 002627 ( Istex/Curation ); précédent : 002626; suivant : 002628Active Handwritten Character Recognition Using Genetic Programming
Auteurs : Ankur Teredesai [États-Unis] ; J. Park [États-Unis] ; Venugopal Govindaraju [États-Unis]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2001.
Abstract
Abstract: This paper is intended to demonstrate the effective use of genetic programming in handwritten character recognition. When the resources utilized by the classifier increase incrementally and depend on the complexity of classification task, we term such a classifier as active. The design and implementation of active classifiers based on genetic programming principles becomes very simple and efficient. Genetic Programming has helped optimize handwritten character recognition problem in terms of feature set selection. We propose an implementation with dynamism in pre-processing and classification of handwritten digit images. This paradigm will supplement existing methods by providing better performance in terms of accuracy and processing time per image for classification. Different levels of informative detail can be present in image data and our proposed paradigm helps highlight these information rich zones. We compare our performance with passive and active handwritten digit classification schemes that are based on other pattern recognition techniques.
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DOI: 10.1007/3-540-45355-5_30
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Park</name><affiliation wicri:level="1"><mods:affiliation>Department of Computer Science Center of Excellence for Document Analysis and Recognition, State University of New York at Buffalo, 14250, Buffalo, NY, USA</mods:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science Center of Excellence for Document Analysis and Recognition, State University of New York at Buffalo, 14250, Buffalo, NY</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: jaehwap@cedar.buffalo.edu</mods:affiliation><country wicri:rule="url">États-Unis</country></affiliation></author><author><name sortKey="Govindaraju, Venugopal" sort="Govindaraju, Venugopal" uniqKey="Govindaraju V" first="Venugopal" last="Govindaraju">Venugopal Govindaraju</name><affiliation wicri:level="1"><mods:affiliation>Department of Computer Science Center of Excellence for Document Analysis and Recognition, State University of New York at Buffalo, 14250, Buffalo, NY, USA</mods:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science Center of Excellence for Document Analysis and Recognition, State University of New York at Buffalo, 14250, Buffalo, NY</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: govind@cedar.buffalo.edu</mods:affiliation><country wicri:rule="url">États-Unis</country></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:5B9F57EAE506DD76AC4C6E5F216B7ADBD195E35A</idno><date when="2001" year="2001">2001</date><idno type="doi">10.1007/3-540-45355-5_30</idno><idno type="url">https://api.istex.fr/document/5B9F57EAE506DD76AC4C6E5F216B7ADBD195E35A/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">002818</idno><idno type="wicri:Area/Istex/Curation">002627</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Active Handwritten Character Recognition Using Genetic Programming</title><author><name sortKey="Teredesai, Ankur" sort="Teredesai, Ankur" uniqKey="Teredesai A" first="Ankur" last="Teredesai">Ankur Teredesai</name><affiliation wicri:level="1"><mods:affiliation>Department of Computer Science Center of Excellence for Document Analysis and Recognition, State University of New York at Buffalo, 14250, Buffalo, NY, USA</mods:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science Center of Excellence for Document Analysis and Recognition, State University of New York at Buffalo, 14250, Buffalo, NY</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: amt6@cedar.buffalo.edu</mods:affiliation><country wicri:rule="url">États-Unis</country></affiliation></author><author><name sortKey="Park, J" sort="Park, J" uniqKey="Park J" first="J." last="Park">J. Park</name><affiliation wicri:level="1"><mods:affiliation>Department of Computer Science Center of Excellence for Document Analysis and Recognition, State University of New York at Buffalo, 14250, Buffalo, NY, USA</mods:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science Center of Excellence for Document Analysis and Recognition, State University of New York at Buffalo, 14250, Buffalo, NY</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: jaehwap@cedar.buffalo.edu</mods:affiliation><country wicri:rule="url">États-Unis</country></affiliation></author><author><name sortKey="Govindaraju, Venugopal" sort="Govindaraju, Venugopal" uniqKey="Govindaraju V" first="Venugopal" last="Govindaraju">Venugopal Govindaraju</name><affiliation wicri:level="1"><mods:affiliation>Department of Computer Science Center of Excellence for Document Analysis and Recognition, State University of New York at Buffalo, 14250, Buffalo, NY, USA</mods:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science Center of Excellence for Document Analysis and Recognition, State University of New York at Buffalo, 14250, Buffalo, NY</wicri:regionArea></affiliation><affiliation wicri:level="1"><mods:affiliation>E-mail: govind@cedar.buffalo.edu</mods:affiliation><country wicri:rule="url">États-Unis</country></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2001</date></imprint><idno type="ISSN">0302-9743</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">5B9F57EAE506DD76AC4C6E5F216B7ADBD195E35A</idno><idno type="DOI">10.1007/3-540-45355-5_30</idno><idno type="ChapterID">30</idno><idno type="ChapterID">Chap30</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: This paper is intended to demonstrate the effective use of genetic programming in handwritten character recognition. When the resources utilized by the classifier increase incrementally and depend on the complexity of classification task, we term such a classifier as active. The design and implementation of active classifiers based on genetic programming principles becomes very simple and efficient. Genetic Programming has helped optimize handwritten character recognition problem in terms of feature set selection. We propose an implementation with dynamism in pre-processing and classification of handwritten digit images. This paradigm will supplement existing methods by providing better performance in terms of accuracy and processing time per image for classification. Different levels of informative detail can be present in image data and our proposed paradigm helps highlight these information rich zones. We compare our performance with passive and active handwritten digit classification schemes that are based on other pattern recognition techniques.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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