A Comprehensive Review of SLR Systems: Challenges, Datasets, and Unresolved Gaps
Artykuł w czasopiśmie
MNiSW
20
Lista 2024
| Status: | |
| Autorzy: | Yerimbetova Aigerim, Berzhanova Ulmeken, Miłosz Marek, Sakenov Bakzhan, Daiyrbayeva Elmira, Cherikbayeva Lyailya |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2026 |
| Wersja dokumentu: | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 8 |
| Wolumen/Tom: | 12 |
| Numer artykułu: | 357 |
| Strony: | 1 - 47 |
| Impact Factor: | 3,8 |
| Efekt badań statutowych | NIE |
| Finansowanie: | This research has been funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. BR24992875). |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 5 sierpnia 2026 |
| Abstrakty: | angielski |
| With the rapid advancement of sensor technologies, automated sign language recognition (SLR) has emerged as a critical enabler of inclusive communication systems for individuals with hearing and speech impairments. Although substantial research effort has been directed toward this domain, existing reviews lack a structured comparison of sensing modalities and do not systematically address the challenges of low-resource sign languages. This paper presents a comprehensive systematic review of sensor-based and multimodal SLR systems, covering 76 publications from 2021 to 2026 selected through a PRISMA 2020 protocol. We propose an original four-category taxonomy encompassing wearable sensor-based, contactless non-visual, vision-based, and multimodal systems, and provide a three-category methodological classification distinguishing conventional, machine learning, and deep learning approaches. The comparative analysis reveals that, despite notable progress, critical challenges persist: the absence of standardized datasets, limited cross-user generalization, insufficient multimodal fusion strategies, and inadequate representation of low-resource sign languages, including Kazakh Sign Language (KSL). The findings of this review establish a structured foundation for future research aimed at developing robust, scalable, and computationally efficient SLR systems |
