Indian Sign Language Recognition Using Classical And Machine Learning Techniques – A Review

Humans need to communicate in order to emphasize their thoughts and feelings, collaborate with others, and elevate society as a whole. A hearing-impaired person uses sign language to communicate and this language develops naturally within them. However, the non-signer community doesn’t somehow acknowledge it and hence this remains as a significant barrier that negatively impacts living quality. To bridge the gap, effective sign-language recognition (SLR) system is required and is still an unsolved research issue. New technologies have been developed for the past few years to counter the problem of recognition and were mainly developed using sensors and hardware equipment based on gloves. As contrary to earlier technology, this review presents that, there is no need for expensive and complex hardware in order to recognize sign language, only a modern device with a camera is sufficient. This is accomplished by using Google’s MediaPipe framework and machine learning techniques. In this paper, we had presented various techniques developed for Indian sign language and our future goal is to deliver a reliable SLR system with computer vision and AI due to its self-learning capabilities and increased accuracy.