Combination of CNN and SVM Classifier for Arabic Handwritten Recognition

Abstract

Handwriting recognition possesses significant practical implications. The variability and lack of structure in individuals’ handwriting pose asignificant obstacle for researchers in this field. This study aims to develop a system that can accurately identify handwritten Arabic characters. To achieve this, we employ two widely used techniques: convolutional neural networks (CNN) and support vector machines (SVM). Three models are proposed, one for each technique, as well as a hybrid model that combines the strengths of both approaches. The results are compared by selecting the optimal parameters for each technique. To assess the performance of the models, we use the MNIST digit database and the AHCD handwritten Arabic character database. The results show the superiority of the hybrid model with an accuracy of 98.21%.