Paper: Experimental Comparison of the Performance of SVMs

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Experimental Comparison of the Performance of SVMs with Different Kernel Functions for Recognizing Arabic Characters

said Ghoniemy, Sayed Fadel, M. Asif

Abstract

A considerable progress in the recognition of Latin and Chinese characters has been achieved. By contrast, Arabic Optical character Recognition is still lagging. This is because Arabic language is a cursive language, written from right to left, and each character has different forms according to its position in the word. Support vector machines using kernel classifiers represent a typical approach for character recognition. Choosing the most appropriate kernel highly depends on the problem at hand – and fine tuning its parameters can easily become a tedious and cumbersome task. The present study is devoted to an experimental comparison of the performance of SVM machines with different kernel functions for recognizing Arabic Characters. Two groups of kernel functions were used throughout the study, each group contains 7 kernel functions. The obtained results show that, in the radial basis group, Laplacian kernel gives the best results. In the special functions group, the T-Student approach gives the best results. However, combing both kernels did not yield better performance.

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Sok, P. and Taing, N., "Support Vector Machine (SVM) Based Classifier For Khmer Printed Character-set Recognition", Asia-Pacific Signal and Information Processing Association, 2014 Annual Summit and Conference (APSIPA) (pp. 1-9). IEEE. December 2014.

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Thanks for cited my Research on SVM related method