Real-Time Gesture-to-Speech Conversion System for Mute Person

This study introduces a machine learning-based system that assists mute individuals by recognizing real time hand gesture using a convolutional neural network (CNN). Captured via a laptop camera, these gestures are converted into text or speech for accessible non-verbal communication. The model is trained on a large dataset, accurately identifies various gestures, such as a raised finger indicating a need for food. A user-friendly camera window dashboard displays real-time results, ensuring practicality for daily use. The system boasts high accuracy and quick response times, making it effective for real-time interactions. Future enhancements may include expanding gesture recognition and improving robustness, advancing assistive technology for better communication.