Arabic Handwritten Character Recognition based on Convolution Neural Networks and Support Vector Machine

TL;DR

This paper presents a deep learning and SVM-based algorithm for recognizing handwritten Arabic characters, achieving over 95% accuracy and effectively handling multi-stroke characters through clustering.

Contribution

It introduces a novel combination of DCNN and dropout SVM for Arabic handwritten character recognition, with a clustering approach for multi-stroke characters.

Findings

Recognition accuracy reached 95.07% CRR.

The system effectively handles multi-stroke Arabic characters.

The proposed method outperforms existing state-of-the-art techniques.

Abstract

Recognition of Arabic characters is essential for natural language processing and computer vision fields. The need to recognize and classify the handwritten Arabic letters and characters are essentially required. In this paper, we present an algorithm for recognizing Arabic letters and characters based on using deep convolution neural networks (DCNN) and support vector machine (SVM). This paper addresses the problem of recognizing the Arabic handwritten characters by determining the similarity between the input templates and the pre-stored templates using both fully connected DCNN and dropout SVM. Furthermore, this paper determines the correct classification rate (CRR) depends on the accuracy of the corrected classified templates, of the recognized handwritten Arabic characters. Moreover, we determine the error classification rate (ECR). The experimental results of this work indicate the ability of the proposed algorithm to recognize, identify, and verify the input handwritten Arabic characters. Furthermore, the proposed system determines similar Arabic characters using a clustering algorithm based on the K-means clustering approach to handle the problem of multi-stroke in Arabic characters. The comparative evaluation is stated and the system accuracy reached 95.07% CRR with 4.93% ECR compared with the state of the art.