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

Published:17 June 2023Publication History
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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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                  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
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                  Publication History

                  • Published: 17 June 2023
                  • Online AM: 10 May 2023
                  • Accepted: 4 May 2023
                  • Revised: 12 April 2023
                  • Received: 20 August 2021
                  Published in tallip Volume 22, Issue 6

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