Towards a Lightweight Arabic Sign Language Translation System.

Mohammed Algabri, Mohamed A Mekhtiche, Mohamed A Bencherif, Fahman Saeed

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

An automated framework for qur'anic education of the hearing-impaired using body pose classification and Arabic sign language integration.Scientific reports · 2026
Article

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Mohammed AlgabriComputer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh 13713, Saudi Arabia.

Mohamed A MekhticheComputer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.

Mohamed A BencherifComputer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.ORCID 0000-0001-8147-8679

Fahman SaeedComputer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.ORCID 0000-0002-7319-8340

Funding

King Salman Center for Disability Research KSRG-2024-467

6 · The paper itself

Abstract

There is a pressing need to build a sign-to-text translation system to simplify communication between deaf and non-deaf people. This study investigates the building of a high-performance, lightweight sign language translation system suitable for real-time applications. Two Saudi Sign Language datasets are used for evaluation. We also investigate the effects of the number of signers and number of repetitions in sign language datasets. To this end, eight experiments are conducted in both signer-dependent and signer-independent modes. A comprehensive ablation study is presented to study the impacts of model components, network depth, and the size of the hidden dimension. The best accuracies achieved are 97.7% and 90.7% for the signer-dependent and signer-independent modes, respectively, using the KSU-SSL dataset. Similarly, the model achieves 98.38% and 96.22% for signer-dependent and signer-independent modes using the ArSL dataset.

Indexed as

Computer SimulationSign LanguageTranslatingDatasets as TopicSaudi Arabiaattention mechanismlightweight modelsigner-dependent and signer-independent modessign language translation

Identifiers

PMID40942930

DOI10.3390/s25175504

PMCPMC12431459

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LicenceCC BY

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