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Persian Handwritten Digit Recognition using Support Vector Machines
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 29 - Number 12 |
| Year of Publication: 2011 |
| Authors: Omid Rashnodi, Hedieh Sajedi, Mohammad Saniee Abadeh |
10.5120/3702-5193
|
Omid Rashnodi, Hedieh Sajedi, Mohammad Saniee Abadeh . Persian Handwritten Digit Recognition using Support Vector Machines. International Journal of Computer Applications. 29, 12 ( September 2011), 1-6. DOI=10.5120/3702-5193
Abstract
In this paper, appropriate features set based on Discrete Fourier Transform coefficients and the box approach have been proposed to achieve higher recognition accuracy, decreasing the features set dimensions and recognition time of Persian numerals. In classification phase, support vector machine (SVM) has been employed as the classifier. Feature sets consists of 154 dimensions, which are the Fourier coefficients in the contour pixels of input image, average angle and distance pixels which are equal to one in each box the box approach. The scheme has been evaluated on 80,000 handwritten samples of Persian numerals. Using 60,000 samples for training, scheme was tested on other 20,000 samples and 98.84% correct recognition rate was obtained.
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