Printed Arabic Characters Classification using A Statistical Approach

Authors

  • Ihab Zaqout Dept. of Information Technology Faculty of Engineering & Information Technology Al-Azhar University – Gaza Gaza Strip, Palestine

DOI:

https://doi.org/10.24297/ijct.v3i1a.2719

Keywords:

Freeman chain coding, character recognition, feature extraction, classification.

Abstract

In this paper, we propose simple classifiers for printed Arabic characters based on statistical analysis. 109 printed Arabic character images are created for each one of transparent, simplified and traditional Arabic fonts. Images are preprocessed by the binarization and followed by sequence of morphological operations. A non-linear filter is applied on the thinned ridge map to extract termination and bifurcation features. The thinned ridge map vectors (TRMVs) are created using a freeman chain code template. The spatial distribution and statistical properties of the extracted features are calculated.

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Author Biography

Ihab Zaqout, Dept. of Information Technology Faculty of Engineering & Information Technology Al-Azhar University – Gaza Gaza Strip, Palestine

I received the B. S. in Computer Science from the University of Al-Fateh, Libya, in 1987 and the M.S. degree in Computer Science from Jordan University, Jordan in 2000 and the Ph.D. in Computer Science from the University of Malaya, Malaysia in 2006. He is currently at the Dept. of Information Technology, Al- Azhar University - Gaza, Palestine as an assistant professor. His main research interests include image processing, pattern recognition, data mining and machine learning.

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Published

2012-08-01

How to Cite

Zaqout, I. (2012). Printed Arabic Characters Classification using A Statistical Approach. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 3(1), 1–5. https://doi.org/10.24297/ijct.v3i1a.2719

Issue

Section

Research Articles