SIGN LANGUAGE RECOGNITION SYSTEM USING ARTIFICIAL INTELLIGENCE AND DEEP LEARNING

This project focuses on converting received text or audio signals into sign language output using advanced technologies. The system accepts both text input and audio input, where the audio is converted into text using a Speech-to-Text (STT) API. Speech recognition systems are commonly categorized into small, medium, and large vocabulary systems based on the number of words they can process. These systems capture voice input and convert it into corresponding textual output through speech processing techniques. The processed text is then mapped to sign language gestures and displayed as sign images or video sequences. The study highlights the importance of language models in improving the accuracy and reliability of speech-to-text conversion, especially in handling noisy sentences and incomplete words. Experimental results show that the system performs better with diverse and randomly selected data, improving overall accuracy and real-time performance in generating sign language outputs