Research articles
ScienceAsia 42 (2016): 46-51 |doi:
10.2306/scienceasia1513-1874.2016.42.046
Character classification framework based on support vector machine and k-nearest neighbour schemes
Teera Siriteerakula,*, Veera Boonjingb, Rutchanee Gullayanona
ABSTRACT: The problem of Thai character classification can be difficult because of the large number of characters and the similarity in the shape of many characters. While previous work combined different fonts to build their classifier, this paper proposes a framework based on support vector machine (SVM) and k-NN schemes to exploit characteristics of each font separately. In this framework, each font is used to train an SVM separately. With the trained SVMs, a vector of predicted values can be produced for any input image. Then a class label of the input image can be found by a k-NN based scheme. The proposed framework performs well with familiar fonts while providing an acceptable performance on unfamiliar fonts.
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| a |
Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520 Thailand |
| b |
International College, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520 Thailand |
* Corresponding author, E-mail: kfteera@kmitl.ac.th
Received 9 Jan 2014, Accepted 0 0000
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