Towards Touchless Interaction: Implementing Hand Gesture Recognition for Presentation and Media Control

The integration of hand gesture recognition with modern multimedia systems offers a novel and intuitive interface for users to interact with. This research implements a reliable framework for gesture-based control of slide presentations and media functionalities, using real-time hand detection and tracking through computer vision technologies such as OpenCV and cvzone. Recognizing individual hand gestures, the system allows for smooth navigation through slides in a presentation, annotating on slides, changing the brightness, managing windows like window minimization and maximization, and controlling media playback. The solution uses machine learning-based hand pose estimation along with optimal gesture mappings to make the recognition highly accurate and responsive. This research is meant to represent the possibility of hands-free interaction in professional and academic settings. The approach also studies applications in remote work scenarios, thereby increasing the incorporation of gesture recognition technology into everyday computing work. Moreover, the system is flexible and scalable and has the potential to integrate with virtual reality systems, remote learning platforms, and interactive kiosks. The proposed system, under controlled conditions, achieves an average accuracy of about 85 percent to 90 percent. It recognizes predefined hand gestures for navigation and interaction. When multiple gestures are shown at the same time, the system might experience temporary lags, but it recovers functionality within a few seconds. Thus, this research underlines the role of gesture recognition technology in transforming the way a user experience is provided.