Machine-printed Arabic OCR

Khosrow M. Hassibi

doi:10.1117/12.169463

25 February 1994 Machine-printed Arabic OCR

Khosrow M. Hassibi

Proceedings Volume 2103, 22nd AIPR Workshop: Interdisciplinary Computer Vision: Applications and Changing Needs; (1994) https://doi.org/10.1117/12.169463
Event: 22nd Applied Imagery Pattern Recognition Workshop, 1993, Washington, DC, United States

Abstract

This paper presents a brief overview of our research in the development of an OCR system for recognition of machine-printed texts in languages that use the Arabic alphabet. The cursive nature of machine-printed Arabic makes the segmentation of words into letters a challenging problem. In our approach, through a novel preliminary segmentation technique, a word is broken into pieces where each piece may not represent a valid letter in general. Neural networks trained on a training sample set of about 500 Arabic text images are used for recognition of these pieces. The rules governing the alphabet and character-level contextual information are used for recombining these pieces into valid letters. Higher-level contextual analysis schemes including the use of an Arabic lexicon and n-grams is also under development and are expected to improve the word recognition accuracy. The segmentation, recognition, and contextual analysis processes are closely integrated using a feedback scheme. The details of preparation of the training set and some recent results on training of the networks will be presented.

Citation Download Citation

Khosrow M. Hassibi "Machine-printed Arabic OCR", Proc. SPIE 2103, 22nd AIPR Workshop: Interdisciplinary Computer Vision: Applications and Changing Needs, (25 February 1994); https://doi.org/10.1117/12.169463

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