A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets

TL;DR

This paper provides a comprehensive review of Sign Language Recognition (SLR), covering different types, modalities, datasets, challenges, and future research directions to advance the field.

Contribution

It offers an extensive overview of SLR research, analyzing progress, identifying gaps, and suggesting future directions for developing more effective real-time SLR models.

Findings

SLR performance is significantly affected by environment and dataset quality.

Recent advances have improved real-time SLR accuracy.

Identified research gaps and future challenges in SLR development.

Abstract

A machine can understand human activities, and the meaning of signs can help overcome the communication barriers between the inaudible and ordinary people. Sign Language Recognition (SLR) is a fascinating research area and a crucial task concerning computer vision and pattern recognition. Recently, SLR usage has increased in many applications, but the environment, background image resolution, modalities, and datasets affect the performance a lot. Many researchers have been striving to carry out generic real-time SLR models. This review paper facilitates a comprehensive overview of SLR and discusses the needs, challenges, and problems associated with SLR. We study related works about manual and non-manual, various modalities, and datasets. Research progress and existing state-of-the-art SLR models over the past decade have been reviewed. Finally, we find the research gap and limitations in this domain and suggest future directions. This review paper will be helpful for readers and researchers to get complete guidance about SLR and the progressive design of the state-of-the-art SLR model