GestureX: A Real-Time Hand Gesture Recognition System for Intuitive Human-Computer Interaction
Hand gesture recognition is a promising field, which gives new ways for human-computer interaction. In this paper, a hand gesture recognition system implemented using MediaPipe and OpenCV is developed. The system includes three functional modules such as Virtual Mouse, Magic Canvas, and MediaPlayer Control. It applies real-time hand tracking and gesture detection capabilities for intuitive, hardware-free interactions. The Virtual Mouse module essentially replaces traditional mouse hardware, while end users use defined hand gestures to control cursor movement and perform mouse operations in a very intuitive way, such as clicking, dragging, and scrolling. The Magic Canvas module forms a creative workspace, including drawing by hand and writing by hand, changing colors, and saving these creations with simple hand gestures as input. The MediaPlayer Control module provides hand-gesture-based controls for enhancing multimedia experiences, including volume and brightness adjustment and even navigation, where it could skip videos forward or backward by a specific time. The integration of modules demonstrates flexibility of hand gesture recognition for practical applications. This goes as far as direct input of a seamless and efficient alternative to other devices. High accuracy and quick response built this system accessible for a wide use case, ranging from accessibility tools to creative applications and multimedia management. This research here emphasizes that gesture-based interaction systems make way for developments that potentially improve user experience.
Authors
- Shivraj Jadhav (ORCID: https://orcid.org/0000-0002-0973-265X)
- Santosh B. Dighe (ORCID: https://orcid.org/0000-0003-3260-2981)
- Rutuja Kulkarni
- Hana Khan
- Priyadarshan Dhabe
- Anchit Chedge
Publication Details
- Published
- 2025-07-04
- DOI
- https://doi.org/10.1109/icctdc64446.2025.11158058
- Citations
- 1
- Primary Topic
- Hand Gesture Recognition Systems
- Type
- article
- Field-Weighted Citation Impact
- 1.94