An Efficient CNN-Based Approach for Real-Time Sign Language Detection

Sign language plays a vital role in communication for individuals with hearing and speech impairments. However, the lack of automated and efficient interpretation systems limits effective interaction with the general population. This project presents an efficient Convolutional Neural Network (CNN)-based approach for real-time sign language detection using image-based hand gesture analysis. The proposed system captures live video frames through a camera, performs preprocessing to enhance relevant features, and classifies static hand gestures using a trained CNN model. The architecture is designed to achieve a balance between recognition accuracy and computational efficiency, making it suitable for real-time applications. Experimental evaluation demonstrates that the system is capable of recognizing sign language gestures with satisfactory accuracy under controlled conditions. The proposed approach highlights the potential of deep learning-based computer vision techniques in developing assistive communication systems.