Department of Electrical and Computer Engineering, Semnan Branch, Islamic Azad University, Semnan, Iran.

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Abstract—In this paper, a method for recognizing online Farsi characters that are written separately has been introduced. Regarding to the shape and the structure of the main body, Farsi letters have been divided into 18 groups. First, hidden Markov model (HMM) technique has been exploited to recognize the main body. In the next step, the final recognition in each group is performed according to delayed strokes (dots and small signs) and their hidden Markov models. The proposed method has been tested on TMU dataset and the recognition accuracy of 95.9% and 94.2% has been obtained for the recognition of the group and the character, respectively.

Index Terms—Character recognition, Farsi, Persian, online handwriting, HMM.

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Cite: Vahid Ghods, Mohammad Karim Sohrabi, "Online Farsi Handwritten Character Recognition Using Hidden Markov Model," Journal of Computers vol. 11, no. 2, pp. 169-175, 2016.