• Dr. Marrynal S Eastaff

This paper aims to create a real-time system that translates sign language into text and speech, facilitating communication between individuals who are deaf or hard of hearing and those unfamiliar with sign language. The system employs computer vision and natural language processing techniques to recognize and interpret sign language gestures captured by a camera-based input system. Through object detection and hand pose estimation algorithms, the system accurately identifies hand gestures, maps them to a predefined sign language dictionary using machine learning, and generates synthesized speech output via text-to-speech technology. This synthesized speech provides an auditory representation of the sign language, enabling non-signers to understand and respond to messages. By effectively bridging the communication gap, this project aims to empower individuals with hearing impairments to communicate inclusively in various social and professional contexts. Key aspects include.

Hand Signs, Computer Vision, Text, and Audio Processing

"Advancements in Real-Time Hand Sign-to-Text Conversion: An Overview", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.d113-d116, April-2024, Available :http://www.jetir.org/papers/JETIR2404317.pdf

"Advancements in Real-Time Hand Sign-to-Text Conversion: An Overview", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppd113-d116, April-2024, Available at : http://www.jetir.org/papers/JETIR2404317.pdf