Helping Hearing Impaired in Problematic Situation Using Deep Learning

Abstract – The “Hand Sign Detection Using Deep Learning Approach” project aims to develop a robust, real-time hand gesture recognition system capable of detecting and classifying static hand signs with high accuracy. The system leverages advanced computer vision techniques combined with deep learning models to process live video streams or uploaded images. By integrating MediaPipe for fast hand detection, OpenCV for preprocessing, and TensorFlow/Keras for inference, the system can operate reliably under varied conditions. The trained model will be stored in multiple formats – .hdf5, .keras, and .tflite – to ensure compatibility across platforms and deployment environments.The deliverable is a fully hosted, interactive website where users can perform gestures in front of their webcam or upload an image to receive real-time predictions. Backend services will be implemented in Python with Flask or FastAPI , and the frontend will be lightweight, responsive, and device-independent. This approach enables broad accessibility, with potential applications in accessibility solutions, educational tools, and hands-free interfaces.