International Research Journal of Education and Technology

Hand gesture recognition offers a natural, touchless method for controlling digital devices, enhancing accessibility and user experience. This project implements a virtual mouse using Python, OpenCV, and MediaPipe to track hand movements through a live video feed, enabling real-time actions like movement, clicks, and screenshots. Leveraging MediaPipe's pre-trained hand landmark models, the system ensures accurate gesture detection, providing responsive, hands-free interaction for users with limited mobility or in touch-restricted environments. With applications in virtual and augmented reality, gaming, and remote control, this project highlights the potential of computer vision and machine learning to deliver efficient, non-invasive human-computer interfaces.