Deep Learning-Based Gesture Recognition for Computer Vision Applications

  • Unique Paper ID: 182954
  • Volume: 12
  • Issue: no
  • PageNo: 25-31
  • Abstract:
  • Natural and touchless communication between humans and machines requires hand gesture detection to serve as the key component for human-computer interaction. This document unites a multilingual translation system with hand gesture detection through real-time MediaPipe and OpenCV processing. The system recognizes gesture inputs to identify them before producing output text as Telugu, Hindi, and French. The model depends on OpenCV image processing functions together with MediaPipe hand-tracking for achieving reliable gesture detection. The translated content output enhances accessibility when dealing with people who use different languages. The translation algorithm shows accurate gesture detection through test experiment results. This research strives to progress available methods that enable nonverbal communication and assistive technologies and human-computer interaction systems.

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{182954,
        author = {Mr. Visvanathan and T. Surya Teja and V. Deepak Sai},
        title = {Deep Learning-Based Gesture Recognition for Computer Vision Applications},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {12},
        number = {no},
        pages = {25-31},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182954},
        abstract = {Natural and touchless communication between humans and machines requires hand gesture detection to serve as the key component for human-computer interaction. This document unites a multilingual translation system with hand gesture detection through real-time MediaPipe and OpenCV processing. The system recognizes gesture inputs to identify them before producing output text as Telugu, Hindi, and French. The model depends on OpenCV image processing functions together with MediaPipe hand-tracking for achieving reliable gesture detection. The translated content output enhances accessibility when dealing with people who use different languages. The translation algorithm shows accurate gesture detection through test experiment results. This research strives to progress available methods that enable nonverbal communication and assistive technologies and human-computer interaction systems.},
        keywords = {},
        month = {},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 12
  • Issue: no
  • PageNo: 25-31

Deep Learning-Based Gesture Recognition for Computer Vision Applications

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