The Development of the Feature Extraction Algorithms for Thai Handwritten Character Recognition System
Identifieur interne : 001818 ( Main/Exploration ); précédent : 001817; suivant : 001819The Development of the Feature Extraction Algorithms for Thai Handwritten Character Recognition System
Auteurs : L. Mitrpanont [Thaïlande] ; Surasit Kiwprasopsak [Thaïlande]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2002.
Abstract
Abstract: This paper presents the development of feature extraction algorithms for the recognition of off-line Thai handwritten characters. These algorithms are used to exploit prominent features of Thai characters. The decision trees were used to classify Thai characters that share common features into five classes then 12 algorithms were developed. As a result, the major features of Thai characters such as an end-point (EP), a turning point (TP), a loop (LP), a zigzag (ZZ), a closed top (CT), a closed bottom (CB), and a number of legs were identified. These features were defined as standard features or the “Thai Character Feature Space.” Then, we defined the 5x3 standard regions used to map these standard features, result in the “Thai Character Solution Space,” which will be used as a fundamental tool for recognition. The algorithms have been tested thoroughly by using of more than 44,600 Thai characters handwritten by 22 individuals from 100 documents. The feature extraction rate is as high as 98.66% with the average of 93.08% while the recognition rate is as high as 99.19% with the average of 91.42%. The results indicate that our proposed algorithms are well established and effective.
Url:
DOI: 10.1007/3-540-48035-8_52
Affiliations:
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Mitrpanont</name><affiliation wicri:level="1"><country xml:lang="fr">Thaïlande</country><wicri:regionArea>Department of Computer Science, Faculty of Science, and Mahidol University Computing Center, Mahidol University, Rama VI Rd., 10400, Bangkok</wicri:regionArea><wicri:noRegion>Bangkok</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Thaïlande</country></affiliation></author><author><name sortKey="Kiwprasopsak, Surasit" sort="Kiwprasopsak, Surasit" uniqKey="Kiwprasopsak S" first="Surasit" last="Kiwprasopsak">Surasit Kiwprasopsak</name><affiliation wicri:level="1"><country xml:lang="fr">Thaïlande</country><wicri:regionArea>Department of Computer Science, Faculty of Science, and Mahidol University Computing Center, Mahidol University, Rama VI Rd., 10400, Bangkok</wicri:regionArea><wicri:noRegion>Bangkok</wicri:noRegion></affiliation><affiliation><wicri:noCountry code="no comma">E-mail: surasitq@hotmail.com</wicri:noCountry></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2002</date></imprint><idno type="ISSN">0302-9743</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">63170D12DBCB33814FCC5F4EA0CA43988D8FF4F4</idno><idno type="DOI">10.1007/3-540-48035-8_52</idno><idno type="ChapterID">52</idno><idno type="ChapterID">Chap52</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 presents the development of feature extraction algorithms for the recognition of off-line Thai handwritten characters. These algorithms are used to exploit prominent features of Thai characters. The decision trees were used to classify Thai characters that share common features into five classes then 12 algorithms were developed. As a result, the major features of Thai characters such as an end-point (EP), a turning point (TP), a loop (LP), a zigzag (ZZ), a closed top (CT), a closed bottom (CB), and a number of legs were identified. These features were defined as standard features or the “Thai Character Feature Space.” Then, we defined the 5x3 standard regions used to map these standard features, result in the “Thai Character Solution Space,” which will be used as a fundamental tool for recognition. The algorithms have been tested thoroughly by using of more than 44,600 Thai characters handwritten by 22 individuals from 100 documents. The feature extraction rate is as high as 98.66% with the average of 93.08% while the recognition rate is as high as 99.19% with the average of 91.42%. The results indicate that our proposed algorithms are well established and effective.</div></front></TEI><affiliations><list><country><li>Thaïlande</li></country></list><tree><country name="Thaïlande"><noRegion><name sortKey="Mitrpanont, L" sort="Mitrpanont, L" uniqKey="Mitrpanont L" first="L." last="Mitrpanont">L. Mitrpanont</name></noRegion><name sortKey="Kiwprasopsak, Surasit" sort="Kiwprasopsak, Surasit" uniqKey="Kiwprasopsak S" first="Surasit" last="Kiwprasopsak">Surasit Kiwprasopsak</name><name sortKey="Mitrpanont, L" sort="Mitrpanont, L" uniqKey="Mitrpanont L" first="L." last="Mitrpanont">L. Mitrpanont</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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