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SIGN LANGUAGE RECOGNITION TO TEXT AND VOICE USING CNN
Communication is one of the fundamental needs of human interaction, but individuals with hearing and speech impairments often face difficulties in communicating with others. Sign language serves as an important medium for such individuals; however, many people are unfamiliar with sign language, creating communication barriers. This project proposes a Sign Language Recognition System using Convolutional Neural Networks (CNN) that converts hand gestures into text and voice output in real time. The system captures hand gestures using a camera, preprocesses the images, and uses deep learning techniques to classify sign language gestures accurately. The recognized gestures are converted into readable text and synthesized speech, enabling effective communication between sign language users and non-sign language users. The proposed system improves accessibility, enhances communication efficiency, and demonstrates the effectiveness of CNN models in imagebased gesture recognition applications.
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