Printed arabic character recognition using HMM

Authors:
Abbas H. Hassin;Xiang-Long Tang;Jia-Feng Liu;Wei Zhao
Affiliations:
Computer Science Department, Harbin Institute of Technology, Harbin 150001, P.R. China;Computer Science Department, Harbin Institute of Technology, Harbin 150001, P.R. China;Computer Science Department, Harbin Institute of Technology, Harbin 150001, P.R. China;Computer Science Department, Harbin Institute of Technology, Harbin 150001, P.R. China
Venue:
Journal of Computer Science and Technology
Year:
2004

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Abstract

The Arabic Language has a very rich vocabulary. More than 200 million people speak this language as their native speaking, and over 1 billion people use it in several religion-related activities. In this paper a new technique is presented for recognizing printed Arabic characters. After a word is segmented, each character/word is entirely transformed into a feature vector. The features of printed Arabic characters include strokes and bays in various directions, endpoints, intersection points, loops, dots and zigzags. The word skeleton is decomposed into a number of links in orthographic order, and then it is transferred into a sequence of symbols using vector quantization. Single hidden Markov model has been used for recognizing the printed Arabic characters. Experimental results show that the high recognition rate depends on the number of states in each sample.