Using Modified Contour Features and SVM Based Classifier for the Recognition of Persian/Arabic Handwritten Numerals

  • Authors:

  • Alireza Alaei;Umapada Pal;P. Nagabhushan

  • Affiliations:

  • -;-;-

  • Venue:
  • ICAPR '09 Proceedings of the 2009 Seventh International Conference on Advances in Pattern Recognition
  • Year:
  • 2009

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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.