Abstract
Conference Title: 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA) Conference Start Date: 2015, Nov. 17 Conference End Date: 2015, Nov. 20 Conference Location: Marrakech, Morocco Support Vector Machines (SVM) is a statistical classification approach which has been successfully applied to various types of problems. However, it has remained largely unexplored for Arabic recognition. SVMs are originally designed for binary classification problems. For multi-class problems, several methods used a combination of binary SVMs while some others solved the problem in one step. This paper introduces an evaluation of five SVM methods for the Arabic characters recognition problem; three are based on binary classifiers, and two considers all classes at once. The selected algorithms are compared in terms of training time, testing time and accuracy. Experiments conducted using the Arabic Printed Text Image Database-Multi-Font(APTID/MF ) showed that the "one-against-one method" is the robust, fast and produces a very good score rate at a reasonable time.