Large Class Arabic Sign Language Recognition

Zakia Saadaoui; Zakia Saadaoui; Rakia Saidi; Fethi Jarray; Fethi Jarray

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Paper

Large Class Arabic Sign Language Recognition

Topics: Natural Language Processing

In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: IC3K, 165-168, 2022 , Valletta, Malta

Authors: Zakia Saadaoui ^{1

;

2} ; Rakia Saidi ¹ and Fethi Jarray ^{1

;

2}

Affiliations: ¹ LIMTIC Laboratory, UTM University, Tunis, Tunisia ; ² Higher Institute of Computer Science of Medenine, Gabes University, Medenine, Tunisia

Keyword(s): Arabic Sign Language, Recognition, CNN, Gesture Recognition.

Abstract: Sign languages are as rich, complex and creative as spoken languages, and consist of hand movements, facial expressions and body language. Today, sign language is the language most commonly used by many deaf people and is also learned by hearing people who wish to communicate with the deaf community. Arabic sign language has been the subject of research activities to recognize signs and hand gestures using a deep learning model. A vision-based system by applying a deep neural network for letters and digits recognition based on Arabic hand signs is proposed in this paper.

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Paper citation in several formats:

Saadaoui, Z.; Saidi, R. and Jarray, F. (2022). Large Class Arabic Sign Language Recognition. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - KEOD; ISBN 978-989-758-614-9; ISSN 2184-3228, SciTePress, pages 165-168. DOI: 10.5220/0011539800003335

@conference{keod22,
author={Zakia Saadaoui. and Rakia Saidi. and Fethi Jarray.},
title={Large Class Arabic Sign Language Recognition},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - KEOD},
year={2022},
pages={165-168},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011539800003335},
isbn={978-989-758-614-9},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - KEOD
TI - Large Class Arabic Sign Language Recognition
SN - 978-989-758-614-9
IS - 2184-3228
AU - Saadaoui, Z.
AU - Saidi, R.
AU - Jarray, F.
PY - 2022
SP - 165
EP - 168
DO - 10.5220/0011539800003335
PB - SciTePress