A robust statistical set of features for Amazigh handwritten characters


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Abstract

The main problem in the handwritten character recognition systems (HCR) is to describe each character by a set of features that can distinguish it from the other characters. Thus, in this paper, we propose a robust set of features extracted from isolated Amazigh characters based on decomposing the character image into zones and calculate the density and the total length of the histogram projection in each zone. In the experimental evaluation, we test the proposed set of features, to show its performance, with different classification algorithms on a large database of handwritten Amazigh characters. The obtained results give recognition rates that reach 99.03% which we presume good and satisfactory compared to other approaches and show that our proposed set of features is useful to describe the Amazigh characters.

About the authors

N. Aharrane

Computer sciences, Imaging and Numerical Analysis Laboratory (LIIAN) USMBA University

Author for correspondence.
Email: aharranenabil@gmail.com
Morocco, Fez

A. Dahmouni

Computer sciences, Imaging and Numerical Analysis Laboratory (LIIAN) USMBA University

Email: aharranenabil@gmail.com
Morocco, Fez

K. El Moutaouakil

Computer sciences, Imaging and Numerical Analysis Laboratory (LIIAN) USMBA University

Email: aharranenabil@gmail.com
Morocco, Fez

K. Satori

Computer sciences, Imaging and Numerical Analysis Laboratory (LIIAN) USMBA University

Email: aharranenabil@gmail.com
Morocco, Fez

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