Real-Time Arabic Sign Language Recognition Using YOLOv5

Zainab Abualhassan, Haidar Ramadhan, Mohammed Faisal Naji, Hajar Alsulaili

2025

Abstract

Sign language is a vital means of communication for the deaf and hard-of-hearing community, yet automatic recognition still faces many challenges. While several sign languages have seen major advances in recognition systems, Arabic sign language (ArSL) remains underdeveloped and requires much more research. Object detection models like YOLOv5 (You Only Look Once, Version 5) have revolutionized computer vision with their high speed, accuracy, and ability to process data in real time. This paper introduces a recognition system leveraging YOLOv5 , a leading object detection model, to classify the 28 letters of the Arabic alphabet. The model was trained on a comprehensive dataset containing thousands of images representing each letter, achieving strong classification results with certain classes reaching perfect accuracy of 100%. To assess the model’s performance, evaluation metrics such as precision, recall, and mean Average Precision (mAP) were employed, demonstrating its practicality for real-world applications. Results indicate that YOLOv5’s architecture, with its efficient feature extraction and real-time processing, reliably handles the complex hand gesture variations in Arabic sign language. Its capability to distinguish subtle differences in hand positions makes it a valuable tool for educational applications, accessibility solutions for the deaf and hard-of-hearing, and future advancements in sign language translation systems. This study contributes a robust Arabic sign language recognition model, addressing an essential need for improved accessibility and communication for Arabic-speaking users.

Paper Citation

in Harvard Style

Abualhassan Z., Ramadhan H., Naji M. and Alsulaili H. (2025). Real-Time Arabic Sign Language Recognition Using YOLOv5. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN , SciTePress, pages 181-187. DOI: 10.5220/0013595200004000

in Bibtex Style

@conference{kdir25,
author={Zainab Abualhassan and Haidar Ramadhan and Mohammed Naji and Hajar Alsulaili},
title={Real-Time Arabic Sign Language Recognition Using YOLOv5},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2025},
pages={181-187},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013595200004000},
isbn={},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Real-Time Arabic Sign Language Recognition Using YOLOv5
SN -
AU - Abualhassan Z.
AU - Ramadhan H.
AU - Naji M.
AU - Alsulaili H.
PY - 2025
SP - 181
EP - 187
DO - 10.5220/0013595200004000
PB - SciTePress