Hand Gesture Controlled System: Enhancing User Interaction Through Intuitive Gestures

This paper presents a comprehensive, gesture-controlled human-computer interaction system designed to en-hance user engagement in educational and professional environments. Utilizing computer vision technologies such as OpenCV, MediaPipe, and Streamlit, the system interprets real-time hand gestures for functionalities including PowerPoint control, virtual smartboard drawing, and mathematical problem-solving via an AI -integrated interface. Key innovations include servo motor-driven camera tracking and face recognition, which is employed to ensure that only the intended user's gestures are recognized, thereby preventing input from surrounding individuals. The system was evaluated with 25 participants comprising students and educators, achieving a gesture recognition accuracy of 94.7% and a face identification accuracy of 95.6 % under standard lighting conditions.