Hand gesture based ai virtual mouse

The rapid advancement of artificial intelligence (AI) and computer vision has paved the way for innovative human-computer interaction techniques. This paper presents an AI-based virtual mouse system that enables users to control a computer using hand gestures, eliminating the need for a physical mouse. The system leverages a Convolutional Neural Network (CNN) algorithm, along with computer vision techniques, to accurately recognize hand gestures and execute corresponding mouse functions such as right-click, left-click, double-click, scrolling, volume control, and drag-and-drop.Developed using Python and OpenCV, the proposed system processes real-time images from a webcam, applies image processing techniques, and extracts key hand features to perform various operations. The CNN model enhances accuracy and adaptability, allowing the system to function effectively across different lighting conditions, backgrounds, and hand sizes. This technology offers a user-friendly and cost-effective alternative to conventional input devices, benefiting individuals with disabilities, professionals seeking a touch-free interface, and general users looking for an intuitive method of interaction. The virtual mouse system is evaluated based on accuracy, speed, and robustness and is compared with existing gesture-based input solutions. The results demonstrate that the system provides efficient and precise control, making it a viable alternative to traditional mice. By improving accessibility and convenience, this AI-powered virtual mouse contributes to the future of human-computer interaction.