**Description** While exploring ways to improve communication accessibility for the Indian deaf community, I came across a project called [OmniBridge](https://www.youtube.com/watch?v=ros3INVOQEU) which uses American Sign Language (ASL) with computer vision for real-time translation. Inspired by this, I propose integrating a similar feature into SUNVA AI by training a model on Indian Sign Language (ISL) using publicly available ISL datasets. This could significantly bridge the communication gap for users who prefer sign language over typing. **Expected Behavior** - A camera-based input interface allows users to sign using ISL. - The system interprets ISL signs in real-time and converts them into simplified text on screen. - Optionally, this text can then be converted to speech using SUNVA AI's existing TTS features. **Current Behavior** SUNVA AI currently supports STT (speech-to-text), text simplification, highlighting, and TTS for typed responses. It does not currently support input through sign language. **Possible Solution** - Explore training a lightweight computer vision model on ISL gesture datasets from [data.gov.in](https://www.data.gov.in/resource/indian-sign-language-dictionary-till-january-2024). - Start with a limited set of frequently used ISL gestures for a proof of concept. - Integrate the model into the SUNVA frontend to allow real-time ISL input. - Engage with the deaf community to validate the usefulness and ease of use of this feature. **Additional Context** As a beginner, I’m unsure how to train the model or integrate it, so I’m raising this here for guidance, suggestions, and possible collaboration. I believe this could be a meaningful step forward for SUNVA AI and its impact on the Indian deaf community.