Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.
Abstract
In this paper a method for online recognition of Farsi handwritten digit is presented. Four sets of Point Features and a set of global features are extracted from preprocessed patterns. In this study a suitable structure for feature vector, which contains only a set of point features and global features, to improve the performance of classifier, is presented. Therefore, numerous experiments with each of the point feature set and the global features using support vector machine (SVM) classifier, with one versus all (OVA) and one versus one (OVO) approaches is done. In this paper, for presenting a fast, accurate and reliable method, SVM classifier with OVO approach is proposed for online recognition of Farsi handwritten digits. This method is applied on online-TMU database. The best recognition rate with point feature set (Δx , Δy)s and global features is achieved. The average recognition rate is 98.08%.
Marzani, M., & Razavi, S. M. (2017). Online Recognition of Farsi Handwritten digits Using SVM Classifier. Journal of Soft Computing and Information Technology, 6(1), 44-51.
MLA
Majid Marzani; Seyyed Mohammad Razavi. "Online Recognition of Farsi Handwritten digits Using SVM Classifier". Journal of Soft Computing and Information Technology, 6, 1, 2017, 44-51.
HARVARD
Marzani, M., Razavi, S. M. (2017). 'Online Recognition of Farsi Handwritten digits Using SVM Classifier', Journal of Soft Computing and Information Technology, 6(1), pp. 44-51.
VANCOUVER
Marzani, M., Razavi, S. M. Online Recognition of Farsi Handwritten digits Using SVM Classifier. Journal of Soft Computing and Information Technology, 2017; 6(1): 44-51.
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