SignVisionAI: Real-Time Sign Language Recognition Using AI

Abstract: Communication is a fundamental necessity for human interaction. However, individuals with hearing and speech impairments often face significant difficulties in expressing themselves and understanding others due to the lack of common communication platforms. Sign language serves as the primary medium of communication for such individuals, but the majority of the population is not trained to understand it. This creates a communication gap, limiting social interaction, education, employment opportunities, and access to public services.

The project “Real-Time Sign Language Recognition Using Artificial Intelligence” aims to bridge this communication gap by developing an intelligent system capable of recognizing hand gestures in real time and converting them into meaningful text or speech output. The proposed system utilizes advanced Artificial Intelligence techniques, particularly Deep Learning models such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), combined with Computer Vision methods for gesture detection and recognition.

The system captures live video input through a webcam, processes the frames using OpenCV and MediaPipe for hand detection and tracking, extracts relevant features, and classifies gestures using trained AI models. The recognized gestures are then translated into text and optionally into speech, enabling seamless and natural communication. The modular design of the system ensures high accuracy, scalability, and flexibility under varying environmental conditions such as lighting and background complexity.

This project contributes significantly to assistive technology by promoting inclusivity and accessibility. It finds applications in education, healthcare, public services, workplaces, and personal communication. By integrating AI, deep learning, and real-time processing, the system provides a cost-effective, efficient, and user-friendly solution that empowers hearing- impaired individuals to communicate independently and confidently in everyday life.

Keywords: Artificial Intelligence, Deep Learning, Convolutional Neural Networks, Computer Vision, MediaPipe, OpenCV, Real-Time Gesture Recognition, Sign Language, Assistive Technology.