HAND GESTURE DRAWING

Akhil Nair , Ninad Gaikwad , Vaishnav Nair , Kenvin Pillai , Riya Kamble

Using OpenCV, this project Hand Gesture Drawing enables intuitive digital art creation through hand gestures. It employs hand tracking and gesture recognition, interpreting real-world movements as drawing commands. Techniques include image processing, contour detection, and machine learning. Users can store notes or clear drawings, enhancing teaching methods. The system captures real-time video input, isolates the hand, and applies tracking models. Machine learning fine-tuning ensures robust gesture recognition. The innovative solution offers an engaging way for users to express themselves. Overall, it combines computer vision and user-friendly interfaces for intuitive digital art creation.

Published Paper   E-Certificate

"HAND GESTURE DRAWING", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.12, Issue 4, page no.a435-a437, April-2025, Available :https://tijer.org/TIJER/papers/TIJER2504066.pdf

Volume 12 Issue 4, April-2025

Pages : a435-a437

Paper Reg. ID: TIJER_156637

Published Paper Id: TIJER2504066

Downloads: 000306

Research Area: Science and Technology

Country: Kalyan, Maharashtra, India

Published Paper PDF: https://tijer.org/TIJER/papers/TIJER2504066

Published Paper URL: https://tijer.org/TIJER/viewpaperforall?paper=TIJER2504066

ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: TIJER (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition