Recognizing Arabic Handwritten Script using Support Vector Machine classifier

Abstract

Handwriting recognition ranks among the highest and the most triumphant applications in the pattern recognition domain. Despite being a developed field, many enquiries are still needed and still represent a defiance mainly for the Arabic Handwritten Script (AHS). Recently, more regard has been given to Support Vector Machines (SVM) classifier for script recognition. Nevertheless, it has not been put in application yet to the handwritten Arabic field if compared with the other methods like ANN, CNN, RNN and HMM. SVMs for AHS recognition is examined in this paper. Handcrafted feature is handled as input by the suggested method and gets going with a supervised learning algorithm. We chose the Multi-class Support Vector Machine with an RBF kernel and we tested it on Handwritten Arabic Characters Database (HACDB) as well. It was proven that the proposed method was effective thanks to the simulation results. We compared the well-functioning of this method with character recognition reliabilities coming from state-of-the-art Arabic OCR which resulted in commendatory outcomes.