Sign Language Prediction Using Deep Learning

This project aims to bridge the communication barrier for individuals who are deaf or hard of hearing by creating a real-time sign language recognition system. The system utilizes a webcam to interpret American Sign Language (ASL), Indian Sign Language (ISL), and British Sign Language (BSL), converting hand gestures into both text and speech to aid those who are not familiar with sign language. To achieve accurate gesture recognition, the system integrates advanced artificial intelligence techniques. It captures live video input and processes it using MediaPipe for detecting hand landmarks. A Convolutional Neural Network (CNN) model is then employed to classify the gestures. The recognized signs are displayed as text on the screen and converted into speech through a text-to-speech (TTS) engine, making communication more seamless. For an improved user experience, the system includes an interactive dashboard that offers real-time hand-tracking visualization, prediction history, user feedback for correction, and performance analysis, including accuracy rates and response times. It also allows for a comparative evaluation of recognition accuracy across ASL, ISL, and BSL. Designed for scalability, the system has the potential to support additional sign languages in the future. By combining computer vision, deep learning, and an intuitive user interface, this project introduces an innovative assistive technology that enhances communication for the deaf and hard-of-hearing community.