Innovative Hand Sign Recognition to Text-and- Speech Conversion System

The rapid advancements in human-computer interaction have paved the way for innovative assistive technologies. This project, "Hand-Sign to Text-and-Speech Conversion System," focuses on developing a real-time hand sign recognition system that translates gestures into text and speech. Utilizing computer vision and machine learning techniques, particularly Convolutional Neural Networks (CNNs), the system aims to bridge the communication gap for individuals with speech and hearing impairments. The project employs OpenCV, MediaPipe, and TensorFlow for hand sign detection and recognition, ensuring high accuracy and real-time processing. The study also explores the challenges, including gesture variability and dataset limitations, while proposing solutions for enhancing system robustness.