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This review aims to systematically analyze sensor-based and multimodal sign language recognition (SLR) systems, focusing on existing challenges and gaps in the literature.

August 7, 2026Journal of ImagingOpen Access

A Comprehensive Review of SLR Systems: Challenges, Datasets, and Unresolved Gaps

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This review aims to systematically analyze sensor-based and multimodal sign language recognition (SLR) systems, focusing on existing challenges and gaps in the literature.
Conducted a systematic review of 76 publications from 2021 to 2026 using the PRISMA 2020 protocol.
Proposed a four-category taxonomy for SLR systems and a three-category methodological classification.
Analyzed challenges related to datasets, cross-user generalization, multimodal fusion, and representation of low-resource sign languages.
Identified critical gaps in existing datasets for low-resource sign languages like Kazakh Sign Language.
Revealed insufficient multimodal fusion strategies affecting SLR system performance.
Documented the need for standardized datasets and improved cross-user generalization to enhance SLR systems.

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Implication

Systematic review analyzes challenges and datasets in sign language recognition systems, suggesting future research needs.

Cite This Study

Yerimbetova et al. (2026) studied this question.

synapsesocial.com/papers/6a758bae847ab6d26c01f12c https://doi.org/10.3390/jimaging12080357

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A Comprehensive Review of SLR Systems: Challenges, Datasets, and Unresolved Gaps

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Discussion

Implication

Cite This Study

A Comprehensive Review of SLR Systems: Challenges, Datasets, and Unresolved Gaps

Key Points

Discussion

Implication

Cite This Study

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