Recent Trends In Sign Language Detection System Using Machine Learning Algorithms

Abstract

This study summarises current advances in sign language recognition systems, emphasising trends, problems, and prospects. Twenty key research publications are analysed, spanning a wide range of sign language recognition topics, from low-power bio-inspired armband solutions to sophisticated neural network structures. These technologies' incorporation into human-robot interactions, as well as their potential to improve communication accessibility, are highlighted. The communication gap limits social integration, access to services, information, and education for individuals who rely on sign language. To address this issue, our research aims to develop an innovative method for real-time translation of sign language into spoken and written languages. We propose a comprehensive system called "SignText" that utilizes Large Language Models (LLMs) and Python programming to interpret and translate sign language into subtitles. Through this approach, we aim to bridge the communication gap and enhance the lives of individuals who use sign language. Initial results demonstrate promising accuracy, with an average recognition rate of over 90% for basic signs and gestures, and up to 85% and 70% for more complex and similar signs, respectively. These results show significant progress toward fostering greater inclusion and accessibility in social interactions and daily life.