Dataset Generation for Egyptian Arabic Sign Language

Mariam Ibrahim, Milad Ghantous, Nada Sharaf

2025

Abstract

This literature review explores the existing body of work related to Egyptian Arabic Sign Language (EASL) datasets, focusing on translation and text-to-video alignment, and examining relevant hand and face landmark detection methodologies, including the use of skeletal joint point analysis. With a particular emphasis on the research gaps in datasets, alignment accuracy, and computer vision models tailored for Arabic dialects, this review aims to highlight the limitations and challenges within current literature. Despite advancements in general sign language research, EASL remains understudied, leaving significant gaps in the development of resources and tools for accurate gesture translation and synchronization. The review concludes by identifying the need for dialect-specific resources and advanced alignment techniques to support the growth of accessible, region-specific sign language datasets.

Paper Citation

in Harvard Style

Ibrahim M., Ghantous M. and Sharaf N. (2025). Dataset Generation for Egyptian Arabic Sign Language. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 1419-1426. DOI: 10.5220/0013380100003890

in Bibtex Style

@conference{icaart25,
author={Mariam Ibrahim and Milad Ghantous and Nada Sharaf},
title={Dataset Generation for Egyptian Arabic Sign Language},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={1419-1426},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013380100003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Dataset Generation for Egyptian Arabic Sign Language
SN - 978-989-758-737-5
AU - Ibrahim M.
AU - Ghantous M.
AU - Sharaf N.
PY - 2025
SP - 1419
EP - 1426
DO - 10.5220/0013380100003890
PB - SciTePress