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Title: Novel curve signatures and a combination method for Thai on-line handwriting character recognition
Authors: Ekawat Chaowicharat
Cercone, Nick
Kanlaya Naruedomkul
Keywords: Curve signatures
Issue Date: 2013
Publisher: Association for the Advancement of Artificial Intelligence
Citation: FLAIRS 2013 - Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference. pp. 196-201.
Abstract: There is no commercial character recognition software that supports Thai handwriting. Thai handwritten character recognition is needed to convert handwritten text written on mobile and tablet devices into computer encoded text. We propose a novel method that joins three curve signatures. The first signature is the normalized tangent angle function (TAF), which provides rough classification. The other two novel curve signatures are the relative position matrix (RPM), which is used to compare global curve features, and the straightened tangent angle function (STAF), which is used to compare the tangent angle along the cumulative unsigned curvature domain. In the recognition process, an input curve is extracted for these three signatures and the similarity against each character in the handwriting templates is measured. Then, the similarity scores are weighted and summed for ranking. Our experiment is done on 48 handwriting sample sets (44 Thai consonants appear in each set, and there are 4 sets per handwriting). Our methods yield an accuracy of 94.08% for personal handwriting, and 92.23% for general handwriting.
ISBN: 978-157735605-9
Appears in Collections:Mathematics: International Proceedings

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