Publication: The development of the feature extraction algorithms for thai handwritten character recognition system
Issued Date
2002-01-01
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ISSN
16113349
03029743
03029743
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2-s2.0-33947153558
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Mahidol University
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SCOPUS
Bibliographic Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol.2358, (2002), 536-546
Suggested Citation
J. L. Mitrpanont, Surasit Kiwprasopsak The development of the feature extraction algorithms for thai handwritten character recognition system. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol.2358, (2002), 536-546. Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/20144
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Title
The development of the feature extraction algorithms for thai handwritten character recognition system
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Abstract
© Springer-Verlag Berlin Heidelberg 2002. 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 5×3 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%1 with the average of 91.42%. The results indicate that our proposed algorithms are well established and effective.