Sign Language Recognition System using ESP32-CAM and Cloud Integration

Overview

This project focuses on accessible communication for Deaf individuals through real-time sign-to-text conversion. Using an ESP32-CAM, images of hand gestures are captured and processed through a lightweight Convolutional Neural Network (CNN). The results are integrated with Supabase cloud functions for real-time inference and stored for monitoring. Outputs are displayed on an LCD, with scope for a mobile app to improve accessibility. The system is designed to be portable, affordable, and scalable, aligning with Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure).