Printed Arabic optical character segmentation

Khader Mohammad; Muna Ayyesh; Aziz Qaroush; Iyad Tumar

doi:10.1117/12.2083587

16 March 2015 Printed Arabic optical character segmentation

Khader Mohammad, Muna Ayyesh, Aziz Qaroush, Iyad Tumar

Proceedings Volume 9399, Image Processing: Algorithms and Systems XIII; 939911 (2015) https://doi.org/10.1117/12.2083587
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

Abstract

A considerable progress in recognition techniques for many non-Arabic characters has been achieved. In contrary, few efforts have been put on the research of Arabic characters. In any Optical Character Recognition (OCR) system the segmentation step is usually the essential stage in which an extensive portion of processing is devoted and a considerable share of recognition errors is attributed. In this research, a novel segmentation approach for machine Arabic printed text with diacritics is proposed. The proposed method reduces computation, errors, gives a clear description for the sub-word and has advantages over using the skeleton approach in which the data and information of the character can be lost. Both of initial evaluation and testing of the proposed method have been developed using MATLAB and shows 98.7% promising results.

Citation Download Citation

Khader Mohammad, Muna Ayyesh, Aziz Qaroush, and Iyad Tumar "Printed Arabic optical character segmentation", Proc. SPIE 9399, Image Processing: Algorithms and Systems XIII, 939911 (16 March 2015); https://doi.org/10.1117/12.2083587

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available