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This paper presents the development and implementation of “Air Canvas,” an innovative system designed for drawing and writing in the air using real-time video processing. Utilizing a standard webcam and leveraging OpenCV’s computer vision capabilities, this system allows users to interactively sketch and write without physical contact with a surface. By detecting and tracking a colored marker on the user’s fingertip, the system interprets hand movements and translates them into digital strokes displayed on a monitor. The core techniques employed include color detection in HSV color space, morphological transformations for refining the detection masks, and contour detection to track the fingertip’s position accurately. This technology not only enhances digital art creation and educational tools by providing a dust-free, touchless interface but also addresses hygienic concerns in shared environments. The proposed framework uses Python for its wide-ranging libraries and straightforward syntax, making the system both robust and user-friendly. This paper discusses the system’s design, including the algorithms for tracking and drawing, challenges encountered in hand gesture recognition, and the potential applications in educational and professional settings. Through this exploration, “Air Canvas” demonstrates significant potential to revolutionize human–computer interaction by making digital interaction more intuitive and accessible.

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Toward Seamless White Boarding: Integrating Machine Learning Techniques with Air Canvas for Enhanced User Experience

  • 会议论文

  • 发表日期：

    2025年

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  • Rajvi Vyas,
  • Shilpa Singhal,
  • Dhara Joshi,
  • Santushti Betgeri,
  • R. N. Ravikumar,
  • Krupali Gosai

摘要

This paper presents the development and implementation of “Air Canvas,” an innovative system designed for drawing and writing in the air using real-time video processing. Utilizing a standard webcam and leveraging OpenCV’s computer vision capabilities, this system allows users to interactively sketch and write without physical contact with a surface. By detecting and tracking a colored marker on the user’s fingertip, the system interprets hand movements and translates them into digital strokes displayed on a monitor. The core techniques employed include color detection in HSV color space, morphological transformations for refining the detection masks, and contour detection to track the fingertip’s position accurately. This technology not only enhances digital art creation and educational tools by providing a dust-free, touchless interface but also addresses hygienic concerns in shared environments. The proposed framework uses Python for its wide-ranging libraries and straightforward syntax, making the system both robust and user-friendly. This paper discusses the system’s design, including the algorithms for tracking and drawing, challenges encountered in hand gesture recognition, and the potential applications in educational and professional settings. Through this exploration, “Air Canvas” demonstrates significant potential to revolutionize human–computer interaction by making digital interaction more intuitive and accessible.