AI-Based Sign Language Recognition System

Abstract: The communication gap between the hearing-impaired community and the hearing population remains a significant challenge, restricting access to seamless integration in modern educational, professional, and social environments. Traditional methods, such as the employment of human sign language interpreters, are often financially prohibitive, geographically unavailable, or limited by real-time processing constraints. This project proposes an innovative AI-Based Sign Language Recognition System that leverages the synergistic capabilities of Deep Learning, Computer Vision, and Convolutional Neural Networks (CNN) to bridge this accessibility gap. Our methodology integrates high-fidelity hand-tracking through the MediaPipe framework, coupled with a custom-trained CNN model specifically optimized for classifying complex sign language alphabets in real- time. The system pipeline includes a robust image processing backbone, a scalable gesture prediction engine, and an intuitive web-based interface that delivers instant text and speech feedback to the user. Extensive performance evaluations and rigorous testing demonstrate that the proposed system achieves exceptional classification accuracy and maintains low-latency inference, even under varying ambient lighting conditions. By providing a scalable, portable, and user-friendly communication tool, this research serves as a viable, modern alternative to expensive sensor-glove hardware, establishing a new foundation for inclusive assistive technologies in digital education.

Keywords: Artificial Intelligence, Computer Vision, Deep Learning, CNN, MediaPipe, OpenCV, Gesture Detection, Real-time Sign Language Recognition, Assistive Technology.