An Intelligent System for Real-Time Sign Language Translation

The increasing need for accessible communication tools has driven advancements in assistive technologies, particularly for individuals with hearing and speech impairments. This paper proposes a real-time sign language translation system integrating machine learning with microcontroller technology. The system captures and processes hand gestures using an image-processing microcontroller, employing a trained model for recognition. Recognized gestures are displayed on a web interface and LCD, with a Text-to-Speech (TTS) module providing audio output. Notably, the system maintains full functionality through local processing via LCD and TTS output even without the web interface, making it suitable for remote areas with limited connectivity. The system ensures accurate, portable, and cost-effective communication, enhancing accessibility in healthcare, education, and public spaces. Future expansions include multilingual support and an extended gesture vocabulary.