Title

Using modified contour features and SVM based classifier for the recognition of Persian/Arabic handwritten numerals

Document Type

Conference publication

Publication details

Alaei, A, Pal, U & Nagabhushan, P 2009, 'Using modified contour features and SVM based classifier for the recognition of Persian/Arabic handwritten numerals', in Proceedings of the Seventh International Conference on Advances in Pattern Recognition, Kolkata, India, 4-6 February, IEEE, USA, pp. 391-394. ISBN: 9780769535203

Published version available from

http://dx.doi.org/10.1109/ICAPR.2009.14

Peer Reviewed

Peer-Reviewed

Abstract

In this paper, we propose a robust and efficient feature set based on modified contour chain code to achieve higher recognition accuracy of Persian/Arabic numerals. In classification part, we employ support vector machine (SVM) as classifier. Feature set consists of 196 dimensions, which are the chain-code direction frequencies in the contour pixels of input image. We evaluated our scheme on 80,000 handwritten samples of Persian numerals. Using 60,000 samples for training, we tested our scheme on other 20,000 samples and obtained 98.71% correct recognition rate. Further, we obtained 99.37% accuracy using five-fold cross validation technique on 80,000 dataset.

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