Abstract—In this paper, we propose a novel method for segmentation of online Persian handwriting into the fundamental building blocks of Persian letters. Employing the findings of our previous work to determine the segmentation points of cursive words and applying some smoothing techniques to improve our results, we have advanced our model to form the pre-segments into the predefined building blocks (BBs) which will be used later for recognizing letters in written words. We have utilized a decision tree to accomplish this task and the 98.6% accuracy has been obtained in forming the BBs as the overall result.
Index Terms—Decision tree, feature extraction, online cursive script, Persian words, segmentation.
It is pleasure to acknowledge the financial support of South Tehran Branch, Islamic Azad University, since this investigation has accomplished in the form of a research plan named “Providing a new feature-based method for online segmentation of Persian words” with encouragement of South Tehran Branch, Islamic Azad University.
Shahriar Pirnia Naeini is with the Department of Computer Science, South Tehran Branch, Islamic Azad University, Tehran 11365/4435, Iran (e-mail: sh_pirnia@azad.ac.ir).
Maryam Khademi is with the Department of Applied Mathematics, South Tehran Branch, Islamic Azad University, Tehran 11365/4435, Iran (e-mail: khademi@azad.ac.ir).
Alireza Nikookar is with the Young Researchers Club, South Tehran Branch, Islamic Azad University, Tehran 11365/4435, Iran (e-mail: a_nikookar@azad.ac.ir).
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Cite: Shahriar Pirnia Naeini, Maryam Khademi, and Alireza Nikookar, "A Novel Approach to Segmentation of Persian Cursive Script Using Decision Tree,"
International Journal of Computer Theory and Engineering vol. 4, no. 3, pp. 465-467, 2012.
