Real Time Sign language Text to Speech Translator
Author(s)
Abstract
Communication barriers faced by deaf and speech-impaired individuals remain a major challenge in society. Sign language is their primary mode of communication; however, most people are not trained to understand it. This project focuses on the design and development of a Real Time Sign Language Text to Speech Translator that converts hand gestures into readable text and audible speech in real time. The proposed system uses computer vision and machine learning techniques to recognize hand gestures captured through a camera.The system processes live video input, detects hand movements, extracts relevant features, and classifies the gestures using a trained deep learning model. Recognized gestures are converted into corresponding text, which is then transformed into speech using a text-tospeech engine. The application aims to provide an efficient and user-friendly solution to bridge the communication gap between hearing-impaired individuals and the general public.Python is used as the core programming language along with libraries such as OpenCV, TensorFlow, and NumPy for image processing and model training. The system was tested in real-time conditions and demonstrated accurate gesture recognition with minimal delay. This solution promotes inclusivity and enables effective communication in public, educational, and professional environments.
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