Learning Web Application for Deaf and Mute with Sign Language Converter

Hearing and speech-impaired individuals rely on sign language as their primary mode of communication; however, most people are not familiar with it, resulting in a significant communication barrier. This project proposes a real-time sign language recognition system that converts hand gestures into both text and speech, enabling effective interaction between deaf and non-deaf individuals. The system uses a vision-based approach, capturing hand gestures through a standard webcam, eliminating the need for specialized hardware such as data gloves. Advanced image processing and computer vision techniques are employed to detect and recognize hand gestures accurately. The system is designed to identify 26 alphabet gestures and translate them into corresponding textual and audio outputs in real time. Additionally, a learning module is integrated, providing video-based guidance for understanding sign language, including alphabets (A–Z), numbers (1–3), and commonly used phrases, making it beneficial for both learners and users. The proposed solution is cost-effective, user-friendly, and can operate on standard computer systems, ensuring wider accessibility. By bridging the communication gap, this system enhances social inclusion, independence, and overall quality of life for hearing and speech-impaired individuals.