Published Paper Details:

Sign language serves as a vital mode of communi- cation for the Deaf and Hard of Hearing (DHH) community, yet barriers persist in its recognition and translation. This project addresses these challenges through innovative technological solu- tions. The primary objective of this study is to develop a robust system for sign language recognition and translation, aiming to enhance communication accessibility for the DHH community. Leveraging advancements in machine learning and computer vision, the project seeks to overcome existing limitations and revolutionize inclusive communication technologies. The project employs a multifaceted approach, integrating Long Short-Term Memory (LSTM) networks and MediaPipe technology to ac- curately detect and interpret sign language gestures. Through extensive training and validation processes, the system is opti- mized to achieve high levels of accuracy and efficiency in real- world scenarios. The developed system demonstrates exceptional performance, achieving a recognition accuracy rate of 98% for sign language gestures. Moreover, it seamlessly translates these gestures into both spoken and written English and Marathi, offering real-time, context-aware translations. This project rep- resents a significant advancement in addressing communication barriers for the DHH community. By providing an accessible and inclusive means of communication, the developed system has the potential to revolutionize interactions and promote equality across diverse domains. The success of this endeavor underscores the importance of leveraging technology to foster inclusivity and enhance the quality of life for individuals with hearing impairments.