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  Published Paper Details:

  Paper Title

Hand gesture Based Virtual Mouse using Machine learning

  Authors

  Neha Bhagwat,  Mansi Dusane,  Moinoddin Inamdar

  Keywords

Gesture Control Virtual Mouse, Virtual Mouse, Hand Gestures, OpenCV

  Abstract


This paper suggests a new method for controlling the cursor that uses hand movements that can be easily recorded by a regular webcam in place of the conventional mouse. This facilitates a hands-free user experience by doing away with the requirement for a physical device. The technology makes use of the webcam's capabilities to track hands accurately. It does more than just mimic hand gestures, though. Certain hand movements will be created for different functions, like dragging and left and right clicks. This allows users to use the cursor and its functionalities in a natural and simple way. For the system to be implemented as described, Python and OpenCV are required. These models, which mimic the swaying motion of human hand movements, have the potential to greatly improve the usability of such computer vision solutions. The world of technology nowadays is always changing due to the abundance of technologies. One such intriguing idea is the human-machine interface. The idea basic this is called signal acknowledgment, which is a technique for reproducing mouse functionalities on a screen without the requirement for any equipment.

  IJCRT's Publication Details

  Unique Identification Number - IJCRTAF02057

  Paper ID - 261068

  Page Number(s) - 283-289

  Pubished in - Volume 12 | Issue 5 | May 2024

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Neha Bhagwat,  Mansi Dusane,  Moinoddin Inamdar,   "Hand gesture Based Virtual Mouse using Machine learning", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 5, pp.283-289, May 2024, Available at :http://www.ijcrt.org/papers/IJCRTAF02057.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


ISSN
ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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