Arabic Algerian Sign Language Translation System Based on 3D Avatar Technology

This work focuses on the creation of tools to help deaf people learn Arabic Algerian Sign Language (ALSL), specifically an automatic translation system with a 3D avatar for accurate and dynamic sign representation. The system encodes ALSL signs using the Notation System Method (NSM), which is principally based on the Hamburg Notation System (HamNoSys). A shallow analysis of the linguistic complexity of ALSL is offered, as well as the obstacles inherent in developing technological solutions to facilitate proper translation. To ensure correctness, the development approach involved working with ALSL translation experts at each level. The system's lexicon, consisting of 417 words, achieved a certified accuracy rate of 75.53%. These results represent a substantial step forward in 3D avatar-based sign language simulation, with opportunities for further refinement and development.

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Publication Details

Published
2024-12-30
DOI
https://doi.org/10.54455/mcn2804
Citations
1
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
0.37
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article

Arabic Algerian Sign Language Translation System Based on 3D Avatar Technology

Taha Zerrouki, Redha Mazari, Mariem Slimani, Amine Mami
1 citations
Hand Gesture Recognition Systems
0.37
article

Arabic Algerian Sign Language Translation System Based on 3D Avatar Technology

Taha Zerrouki, Redha Mazari, Mariem Slimani, Amine Mami
article en
1 citations

Abstract

This work focuses on the creation of tools to help deaf people learn Arabic Algerian Sign Language (ALSL), specifically an automatic translation system with a 3D avatar for accurate and dynamic sign representation. The system encodes ALSL signs using the Notation System Method (NSM), which is principally based on the Hamburg Notation System (HamNoSys). A shallow analysis of the linguistic complexity of ALSL is offered, as well as the obstacles inherent in developing technological solutions to facilitate proper translation. To ensure correctness, the development approach involved working with ALSL translation experts at each level. The system's lexicon, consisting of 417 words, achieved a certified accuracy rate of 75.53%. These results represent a substantial step forward in 3D avatar-based sign language simulation, with opportunities for further refinement and development.

Vol. 9(28)
University of Bouira (DZ), University Yahia Fares of Medea (DZ)
Openalex Percentile: Top 34%
Hand Gesture Recognition Systems
0.37
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