Classifying Arabic handwriting using convolutional neural network

Anas Quteishat (Al-Ahliyya Amman University)
Asharaf Al Sharah (Al-Balqa Applied University)
Ahmed Qtaishat (Sohar University)
Tareq Alawneh (Al-Balqa Applied University)

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 15, No 5: October 2026

This paper presents a classification of Arabic handwritten letters using convolutional neural networks (CNNs). The paper aims to address the unique challenges of recognizing Arabic letters due to their special characteristics such as dots and similarities. Leveraging deep learning, particularly CNNs, the project employs an architecture specifically designed for image classification tasks, which is well-suited for optical character recognition (OCR) of Arabic handwritten letters. The dataset used consists of images collected from the "Arabic handwritten alphabets, words, and paragraphs" (AHAWP) and "Arabic handwritten character database" (AHCDB), which have been carefully processed to ensure uniformity and diversity. The model was built using deep network designer, a tool in MATLAB, and trained over three epochs to prevent overfitting, achieving an accuracy of 89.2%.