Research Article
A Real-Time Libyan Sign Language Recognition Using Deep Learning Method with Vocal Feedback.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 187 - Issue 23 |
| Published: July 2025 |
| Authors: Alhaam Alariyibi, Rana Faraj Amsaad, Abdelsalam Maatuk |
10.5120/ijca2025925378
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Alhaam Alariyibi, Rana Faraj Amsaad, Abdelsalam Maatuk . A Real-Time Libyan Sign Language Recognition Using Deep Learning Method with Vocal Feedback.. International Journal of Computer Applications. 187, 23 (July 2025), 11-21. DOI=10.5120/ijca2025925378
@article{ 10.5120/ijca2025925378,
author = { Alhaam Alariyibi,Rana Faraj Amsaad,Abdelsalam Maatuk },
title = { A Real-Time Libyan Sign Language Recognition Using Deep Learning Method with Vocal Feedback. },
journal = { International Journal of Computer Applications },
year = { 2025 },
volume = { 187 },
number = { 23 },
pages = { 11-21 },
doi = { 10.5120/ijca2025925378 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2025
%A Alhaam Alariyibi
%A Rana Faraj Amsaad
%A Abdelsalam Maatuk
%T A Real-Time Libyan Sign Language Recognition Using Deep Learning Method with Vocal Feedback.%T
%J International Journal of Computer Applications
%V 187
%N 23
%P 11-21
%R 10.5120/ijca2025925378
%I Foundation of Computer Science (FCS), NY, USA
Abstract
There are over 12,000 deaf and hearing-impaired individuals in Libya, according to 2018 statistics from the Social Solidarity Fund. Despite this significant population, access to effective communication tools remains limited. Deep learning has revolutionized various domains, and its impact on the recognition and translation of sign languages is no exception. This paper explores the application of deep learning, particularly Long Short-Term Memory (LSTM) networks, in the context of Libyan Sign Language (LSL) recognition and translation, aiming to bridge communication barriers for the hearing-impaired community in Libya. The paper presents a novel dataset and a robust LSL recognition model based on LSTM architecture and key point extraction using MediaPipe Holistic. Furthermore, the real-time testing showcases the practicality of the proposed LSL recognition model, offering the potential for real-world applications to empower the deaf community. The proposed LSTM model achieves an impressive testing accuracy of 84% in recognizing LSL gestures and translating them into Spoken Arabic. This work is a critical milestone in enhancing accessibility and empowering the deaf community in Libya.
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