Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Tareq Alawneh
Al-Balqa Applied University
Computer Science & IT Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Classifying Arabic handwriting using convolutional neural network Anas Quteishat; Asharaf Al Sharah; Ahmed Qtaishat; Tareq Alawneh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4015-4025

Abstract

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%.
Co-Authors Ahmed Qtaishat Anas Quteishat Asharaf Al Sharah