Towards a generic M-SVM parameters estimation using overlapping swarm intelligence for handwritten characters recognition

Abstract

Support vector machines (SVM) is a statistical classification approach which has been successfully applied to solve various types of problems in pattern recognition. However, it has remained largely unexplored for Arabic recognition. It has been proved to be a good tool for multi-classification issues related to machine learning. But, the performance of the SVM depends solely upon the appropriate choice of parameters. Hence, particle swarm optimization (PSO) technique is employed in tuning SVM parameters. The proposed SVM-PSO model is used to solve the Arabic characters recognition problem. The selected models are compared in terms of the testing time and accuracy. This study employs support vector machines in the Isolated Farsi/Arabic Character Database (IFHCDB) recognition. Experimental results have proven that PSO could be a good alternative for predicting SVM parameters.