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@article{195442,
        author = {Puccha Poojitha and Jaladi Abishek and Pedhapeniki Durga Prasad and Koppada Vinay and K .Srinivasa Rao},
        title = {REAL-TIME HAND GESTURE RECOGNITION USING MEDIAPIPE LANDMARK DETECTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {173-175},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195442},
        abstract = {Hand gesture recognition is a crucial area in human–computer interaction that enables natural and touchless communication between users and digital systems. Traditional input devices such as keyboards and mice limit intuitive interaction. This project presents a real-time hand gesture recognition system using MediaPipe landmark detection to provide a more efficient interface. The system applies computer vision and machine learning techniques to detect and classify hand gestures from live webcam input. MediaPipe is used to extract 21 key hand landmarks representing joints and fingertips. By analyzing these landmarks, gestures such as open palm, fist, thumbs up, and finger counting are recognized in real time. The system processes video frames, detects the hand, extracts landmark coordinates, and identifies gestures using rule-based or learning-based methods. The implementation is carried out using Python, OpenCV, and MediaPipe libraries. The system achieves high accuracy with minimal latency and can be applied in areas such as virtual control systems, gaming, sign language recognition, robotics, and touchless interfaces.},
        keywords = {MediaPipe, Hand Gesture Recognition, Computer Vision, Human–Computer Interaction (HCI), Python, Machine Learning, OpenCV, Landmark Detection, Real-Time Processing, BlazePalm.},
        month = {April},
        }