A comparative study of multi-class support vector machine methods for Arabic characters recognition
Marwa Amara, Khaled Ghédira, Kamel Zidi, Salah Zidi · 2015
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.