Machine Learning Based Sign Language Recognition System

This research paper represents a sign language recognition system that uses computer vision and machine learning algorithms to translate sign language into written or spoken language. The system can recognize and analyse hand and body movements from video footage, and can associate them with corresponding words or phrases in the target language. The system is trained on large datasets of sign language videos, and can be customized to recognize different sign languages and dialects as well as it also converts text into speech. The paper also discusses the various technologies involved in implementing the system, including depth sensors, cameras, and microphones. The research paper presents the results of experiments conducted to evaluate the performance of the system, including its accuracy and speed of translation. The paper also explores the potential applications of the system, including improving communication between the deaf and hearing communities, and enabling deaf people to access a wider range of information and services. Overall, the research paper demonstrates the potential of sign language recognition systems to improve accessibility and inclusivity for the deaf and hard-of-hearing communities and highlights the importance of ongoing research and development in this area.