Sign Language Recognition: A Comprehensive Review of Traditional and Deep Learning Approaches, Datasets, and Challenges

Abstract

The Deaf are a large social group in society. Their unique way of communicating through sign language is often confined within their community due to limited understanding by individuals outside of this demographic. This is where sign language recognition (SLR) comes in to help people without hearing impairments understand the meaning of sign language. In recent years, new methods of sign language recognition have been developed and achieved good results, so it is necessary to make a summary. This review mainly focuses on the introduction of sign language recognition techniques based on algorithms especially in recent years, including the recognition models based on traditional methods and deep learning approaches, sign language datasets, challenges and future directions in SLR. To make the method structure clearer, this article explains and compares the basic principles of different methods from the perspectives of feature extraction and temporal modelling. We hope that this review will provide some reference and help for future research in sign language recognition.

Keywords

• Sign language recognition
• traditional method
• deep learning
• SLR datasets