بازشناسی برخط حروف مجزای دست‌نویس فارسی بر اساس تشخیص گروه بدنه اصلی با استفاده از ماشین بردار پشتیبان

In this paper a new method for the online recognition of handwritten Persian characters has been proposed which uses a set of simple features and Support Vector Machine (SVM) as a classifier. The task of preprocessing allows us to equalize feature vectors from different characters. This algorithm is implemented in two steps. In the first step, input character is classified into one of eighteen groups of main strokes of characters and in the second step, position, number, and the shape of sub-strokes determine character type. For example to recognize the character ‘ت’, in the first step the character will be classified to group of letters ‘ب، پ، ت، ث’ based on main stroke shape and then classification is done using information of the sub-strokes. In the final step, post processing, we rectify previous step results employing unmatched conditions between main stroke and sub-strokes. Consider a main stroke «ل» with a point at the top of that in this situation post processing step will change result to letter «ن». The experimental results -which is based on Online-TMU database- show that the recognition rate of the main strokes of the characters is 94% which reaches to 98% using the information of sub-strokes.