Guided classification for Arabic Characters handwritten Recognition

Abstract

Arabic text recognition is a difficult task due to the cursive nature of the Arabic writing system, the different forms of Arabic characters in words, the large number of ligatures, and many other challenges. Deep Learning models have made significant progress in many fields, especially in the field of Optical Characters Recognition (OCR). This article presents a model capable of recognizing handwritten Arabic characters based on deep learning. The proposed model uses Convolutional Neural Networks (CNNs) to divide the 28 Arabic characters into subclasses to improve the classification phase in OCR. The model was tested on the Handwritten Arabic Characters Database (HACDB) dataset and it gave 98% of recognition rates.