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

Abstract

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.