Hand Gesture Detection

Author

Kushal Nana Mali, Prof. P.P.Nayakal

Abstract

 Hand Gesture Detection has become an important technology for enabling natural and touchless interaction between humans and computers. The proposed system detects and recognizes different hand gestures in real time using a webcam and Python-based libraries. The captured video frames are processed to identify the user's hand, extract hand landmarks, and classify predefined gestures such as open palm, fist, thumbs up, and victory sign. The system is designed to provide high detection accuracy with low processing time while maintaining real-time performance. It eliminates the need for external sensors and expensive hardware, making it suitable for educational, healthcare, gaming, and automation applications. Experimental evaluation shows that the proposed system performs efficiently under normal lighting conditions and accurately recognizes hand gestures. The developed model provides a simple, cost-effective, and user-friendly solution for gesture-based interaction and can be extended to advanced applications such as sign language interpretation, virtual reality, and smart home automation.

Keywords

Hand Gesture Detection, Python, OpenCV, MediaPipe, Gesture Recognition, Human-Computer Interaction.

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References

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