New statistical method for machine-printed Arabic character recognition

Hua Wang; Xiaoqing Ding; Jianming Jin; M. Halmurat

doi:10.1117/12.586491

17 January 2005 New statistical method for machine-printed Arabic character recognition

Hua Wang, Xiaoqing Ding, Jianming Jin, M. Halmurat

Proceedings Volume 5676, Document Recognition and Retrieval XII; (2005) https://doi.org/10.1117/12.586491
Event: Electronic Imaging 2005, 2005, San Jose, California, United States

Abstract

Although about 300 million people worldwide, in several different languages, take Arabic characters for writing, Arabic OCR has not been researched as thoroughly as other widely used characters (Latin or Chinese). In this paper, a new statistical method is developed to recognize machine-printed Arabic characters. Firstly, the entire Arabic character set is pre-classified into 32 sub-sets in terms of character forms (Isolated, Final, Initial, Medial), special zones (divided according to the headline and the baseline of a text line) that characters occupy and component information (with or without secondary parts, say, diacritical marks, movements, etc.). Then 12 types of directional features are extracted from character profiles. After dimension reduction by linear discriminant analysis (LDA), features are sent to modified quadratic discriminant function (MQDF), which is utilized as the final classifier. At last, similar characters are discriminated before outputting recognition results. Selecting involved parameters properly, encouraging experimental results on test sets demonstrate the validity of proposed approach.

Citation Download Citation

Hua Wang, Xiaoqing Ding, Jianming Jin, and M. Halmurat "New statistical method for machine-printed Arabic character recognition", Proc. SPIE 5676, Document Recognition and Retrieval XII, (17 January 2005); https://doi.org/10.1117/12.586491

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