Arabic OCR: toward a complete system

Ahmed M. El-Bialy; Ahmed H. Kandil; Mohamed Hashish; Sameh M. Yamany

doi:10.1117/12.373509

22 December 1999 Arabic OCR: toward a complete system

Ahmed M. El-Bialy, Ahmed H. Kandil, Mohamed Hashish, Sameh M. Yamany

Proceedings Volume 3967, Document Recognition and Retrieval VII; (1999) https://doi.org/10.1117/12.373509
Event: Electronic Imaging, 2000, San Jose, CA, United States

Abstract

Latin and Chinese OCR systems have been studied extensively in the literature. Yet little work was performed for Arabic character recognition. This is due to the technical challenges found in the Arabic text. Due to its cursive nature, a powerful and stable text segmentation is needed. Also; features capturing the characteristics of the rich Arabic character representation are needed to build the Arabic OCR. In this paper a novel segmentation technique which is font and size independent is introduced. This technique can segment the cursive written text line even if the line suffers from small skewness. The technique is not sensitive to the location of the centerline of the text line and can segment different font sizes and type (for different character sets) occurring on the same line. Features extraction is considered one of the most important phases of the text reading system. Ideally, the features extracted from a character image should capture the essential characteristics of this character that are independent of the font type and size. In such ideal case, the classifier stores a single prototype per character. However, it is practically challenging to find such ideal set of features. In this paper, a set of features that reflect the topological aspects of Arabia characters is proposed. These proposed features integrated with a topological matching technique introduce an Arabic text reading system that is semi Omni.

Citation Download Citation

Ahmed M. El-Bialy, Ahmed H. Kandil, Mohamed Hashish, and Sameh M. Yamany "Arabic OCR: toward a complete system", Proc. SPIE 3967, Document Recognition and Retrieval VII, (22 December 1999); https://doi.org/10.1117/12.373509

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