This paper presents a Real-Time Sign Language Recognition and Translation System that converts hand gestures into text and speech using AI and computer vision. The system uses Google MediaPipe for 21-point hand landmark detection, a custom Convolutional Neural Network (CNN) for gesture classification, and Text-to-Speech (TTS) synthesis — requiring only a standard webcam, with no specialized hardware needed. A custom dataset of 15 commonly used signs was collected from 5 users, yielding 15,000 samples. The CNN achieved 96% training accuracy and 91% validation accuracy with an average response time of 250ms. The system was deployed as a web application (Next.js + TensorFlow.js), desktop app (Python), and REST API (FastAPI). Usability testing with 12 participants produced a System Usability Scale (SUS) score of 84.2 (Excellent). The system targets real-world use in education, healthcare, and public services, promoting accessibility and inclusion for the hearing-impaired community. Developed as a B.Tech (CSE) final year project at Shri Ramswaroop Memorial University, Lucknow
Singh et al. (Thu,) studied this question.