research-article

Alabib-65: A Realistic Dataset for Algerian Sign Language Recognition

Authors:

Rachid SeghirAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 22, Issue 6

Article No.: 178, Pages 1 - 23

https://doi.org/10.1145/3596909

Published: 17 June 2023 Publication History

Abstract

Sign language recognition (SLR) is a promising research field that aims to blur boundaries between Deaf and hearing people by creating a system that can transcribe signs into a written or vocal language. There is a growing body of literature that investigates the recognition of different sign languages, especially American sign language. So far, to the best of our knowledge, no study has considered the Algerian SLR. This is mainly due to the lack of datasets. To address this issue, we created the Alabib-65, the first Algerian Sign Language dataset. It consists of up to 6,238 Videos recorded from 41 native signers under realistic settings. This dataset is challenging due to a variety of reasons. First, there is a little inter-class variability. The 65 sign classes are similar in terms of hands’ configuration, placement, or movement and can share the same sub-parts. Second, there is a large intra-class variability. Furthermore, compared to other SL datasets that were collected from an indoor environment with a static and simple background, our videos were recorded from both indoor and outdoor environments with 22 backgrounds varying from simple to cluttered, and from static to dynamic. To underpin future research, we provided baseline results on this new dataset using state-of-the-art machine learning methods, namely: IDTFs with Fisher vector and SVM-classifier, VGG16-GRU, I3D, I3D-GRU, and I3D-GRU-Attention. The results show the validity and the challenges of our dataset.

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Cited By

Bellil LGhiri MBoulesnane A(2024)Empowering Deaf Community in Healthcare Communication: 1D-CNN-Based Algerian Sign Language Recognition System2024 6th International Conference on Pattern Analysis and Intelligent Systems (PAIS)10.1109/PAIS62114.2024.10541233(1-7)Online publication date: 24-Apr-2024
https://doi.org/10.1109/PAIS62114.2024.10541233
Alharthi NAlzahrani S(2023)Vision Transformers and Transfer Learning Approaches for Arabic Sign Language RecognitionApplied Sciences10.3390/app13211162513:21(11625)Online publication date: 24-Oct-2023
https://doi.org/10.3390/app132111625
Sahu SPalisetti SPeddinti HYalamati SVajja PRushyasrungu G(2023)Comprehensive study of Sign Language Conversion Using Machine Learning2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)10.1109/UPCON59197.2023.10434799(1069-1075)Online publication date: 1-Dec-2023
https://doi.org/10.1109/UPCON59197.2023.10434799

Index Terms

Alabib-65: A Realistic Dataset for Algerian Sign Language Recognition

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cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 22, Issue 6

June 2023

635 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3604597

Editor:
Imed Zitouni
Google, USA

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Publication History

Published: 17 June 2023

Online AM: 10 May 2023

Accepted: 04 May 2023

Revised: 12 April 2023

Received: 20 August 2021

Published in TALLIP Volume 22, Issue 6

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Cited By

Bellil LGhiri MBoulesnane A(2024)Empowering Deaf Community in Healthcare Communication: 1D-CNN-Based Algerian Sign Language Recognition System2024 6th International Conference on Pattern Analysis and Intelligent Systems (PAIS)10.1109/PAIS62114.2024.10541233(1-7)Online publication date: 24-Apr-2024
https://doi.org/10.1109/PAIS62114.2024.10541233
Alharthi NAlzahrani S(2023)Vision Transformers and Transfer Learning Approaches for Arabic Sign Language RecognitionApplied Sciences10.3390/app13211162513:21(11625)Online publication date: 24-Oct-2023
https://doi.org/10.3390/app132111625
Sahu SPalisetti SPeddinti HYalamati SVajja PRushyasrungu G(2023)Comprehensive study of Sign Language Conversion Using Machine Learning2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)10.1109/UPCON59197.2023.10434799(1069-1075)Online publication date: 1-Dec-2023
https://doi.org/10.1109/UPCON59197.2023.10434799

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