Hand Gesture Canvas-a New Digital Drawing Experience

Abstract

As technology continues to advance, virtual control through human gestures is becoming more prevalent across various sectors. In digital drawing applications, traditional tools like a mouse, stylus, or touchscreen can feel restrictive, making it difficult for users to freely express their creativity. Many users have a desire to create or draw just by moving their hand without relying on physical input devices. The Hand Gesture Canvas project addresses this need by developing a motion-to-text converter that uses real-time hand tracking to enable the hand to act as a pen. This allows users to draw shapes, write text, and clear the canvas using simple hand gestures. The system is built using the Open Computer Vision Library (OpenCV) and MediaPipe, which work together to capture and interpret hand movements through a camera. By integrating computer vision, machine learning algorithms, and augmented reality, the project is able to detect and translate physical gestures into digital drawings in real time. This innovative approach provides a powerful and accessible means of communication, especially for the deaf, by reducing the need for traditional input methods like mobile phones or laptops. Moreover, the Hand Gesture Canvas offers intuitive interaction and has promising applications in fields such as education, remote collaboration, and interactive installations. Future improvements will focus on enhancing the accuracy of gesture recognition and expanding system features, potentially transforming how users interact with digital content.

Keywords: Hand Gesture Recognition, Real-Time Hand Tracking, OpenCV, MediaPipe, Virtual Control