Sign Language Recognition System

Communication between hearing-impaired individuals and the general population remains a significant challenge due to the limited understanding of sign language. This paper presents a real-time sign language recognition system that utilizes computer vision and deep learning techniques to interpret hand gestures and convert them into readable text. The proposed system captures live video input through a webcam, processes the hand region using image preprocessing techniques, and classifies gestures using a Convolutional Neural Network (CNN). The system is designed to ensure high accuracy, low latency, and efficient real-time performance. A structured dataset of hand gestures is used for training, and the model is optimized to handle variations in lighting conditions, hand orientation, and background noise. The system enables continuous gesture recognition and provides immediate textual output, thereby facilitating seamless communication. The proposed solution is scalable and can be extended to support voice output and sentence formation, making it suitable for real-world assistive applications. Experimental results demonstrate the effectiveness of the system in improving accessibility and reducing communication barriers.

Sign Language Recognition, Computer Vision, Deep Learning, CNN, Real-Time Detection, Assistive Technology, Gesture Recognition