Hand Gesture Recognition for Interactive On-Screen Drawing with Real-Time Subtitling for Teaching

This research presents the development of an innovative educational tool leveraging hand gesture recognition for interactive on-screen drawing with real-time subtitling. Built on OpenCV and Python, the platform enables educators to draw and manipulate digital content through intuitive hand gestures, enhancing classroom engagement. Additionally, real-time subtitle generation offers accessibility for hearing-impaired students by converting drawn content into text notes. This system addresses key challenges in traditional digital drawing tools, such as limited interaction methods and accessibility, aiming to deliver a more inclusive learning environment. Preliminary results indicate high recognition accuracy, smooth interaction, and potential for broader educational applications. Future improvements will involve expanding the gesture set and refining accuracy for complex gestures. Keywords: Hand Gesture Recognition, Interactive Learning, Real-Time Subtitling, Educational Technology, OpenCV