This is an advanced computer vision project that leverages MediaPipe and OpenCV to recognize and interact with hand landmarks in real-time. This project captures video from a webcam, processing each frame to identify and visualize key points on a hand, providing a robust solution for gesture recognition and other interactive applications. Using the MediaPipe Hands module, the program detects various points on the hand, such as the tips and metacarpophalangeal (MCP) joints of the fingers, and draws these landmarks on the video feed using OpenCV, offering a clear visualization of the hand's structure. Key features include real-time hand detection, ensuring immediate tracking of hand movements, and landmark visualization, which highlights each detected landmark on the video feed for easy identification of finger tips and joints. The project also includes interactive elements, such as counting the number of fingers extended based on their positions relative to their MCP joints, and sending signals via serial communication when specific conditions are met, such as when a certain number of fingers are extended. This serial communication capability allows integration with other hardware devices, such as an Arduino. Implemented using Python, the project utilizes OpenCV for video processing, MediaPipe for hand landmark detection, and the serial module for communication with external devices. The program continuously updates the positions of the hand landmarks and performs checks to determine the number of extended fingers, displaying this count on the video feed.