A Comparative Analysis of Sign Language Recognition Approaches Across Varied Sign Languages

The purpose of this research is to compare several approaches to Sign Language Recognition across various sign languages. People communicate with one another in their native languages in daily life, and the languages will change as the regions do. Sign language is a form of communication used by those who are unable to speak or write. It might be difficult to communicate be- tween people who can speak and write but cannot speak or write; hence, Sign Language Recognition is necessary. Sign Language Recognition can be done using a glove-based or computer vision-based. There are various approaches used in various sign languages. The proposed approach is computer vision-based to recognize Gujarati Sign Language Characters. Challenges to the suggested approach include the absence of sign language standardization, the lack of a dataset to test the machine learning model, the authors’ limited understanding of sign languages, the impossibility of recognizing sign languages in environments without constraints, the use of similar signs for various characters, and the difficulty of recognizing hand gestures because some sign languages use both hands for gestures.