Enhancing Human-Computer Interaction With Real-Time Hand Gesture Translation

Using cutting-edge computer vision and machine learning techniques, this research study suggests a real-time system for hand gesture recognition and text conversion using OpenCV and MediaPipe. The system processes live video input, detects hand movements, and translates specific gestures into corresponding text outputs. By leveraging a deep learning-based hand tracking model and a rule-based gesture classification method, the approach ensures efficient and accurate recognition. The primary applications of this system include enhancing human-computer interaction, assisting individuals with speech impairments, and facilitating non-verbal communication. The methodology involves capturing hand movements using a webcam, identifying key landmarks, and classifying gestures into predefined categories. Experimental results demonstrate the system’s effectiveness under various lighting conditions, achieving reliable performance for simple gestures. Future improvements will focus on integrating advanced machine learning models to expand gesture recognition capabilities and improve accuracy. This work highlights the potential of computer vision-based gesture recognition for real-world applications in assistive technology and automation.