Investigating the Effect of Different Kernel Functions on the Performance of SVM for Recognizing Arabic Characters

A considerable progress in the recognition techniques of Latin and Chinese characters has been achieved. By contrast, Arabic Optical character Recognition is still lagging in spite that the interest and research in this area is becoming more intensive than before. This is because the Arabic is a cursive language, written from right to left, each character has two to four different forms according to its position in the word, and several characters are associated with complementary parts above, below, or inside the character. Support Vector Machines (SVMs) are used successfully for recognizing Latin, and Chinese characters. This paper studies the effect of different kernel functions on the performance of SVMs for recognizing Arabic characters. Eleven different kernel functions are used throughout this study. The objective is to specify which type of kernel functions gives the best recognition rate. The resulting kernel functions can be considered as base for future studies aiming at enhancing their performance. The obtained results show that Exponential and Laplacian Kernels give excellent performance, while others, like multi-quadric kernel, fail to recognize the characters, speciallywith increased level of noise.