Feature Extraction for Online Farsi Characters

Authors:
Vahid Ghods;Ehsanollah Kabir
Affiliations:
-;-
Venue:
ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
Year:
2010

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Cited 2

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Abstract

This paper demonstrates the effectiveness of proper and efficient features for classifying online Farsi characters. We use these features to classify the main body of Farsi letters to nine groups. We implemented our method on the main bodies of 4000 isolated letters from "TMU dataset". Correct recognition rates of 99% and 94% were achieved for training and test sets respectively.