real time Hand Gesture Recognition

  • Unique Paper ID: 169402
  • Volume: 11
  • Issue: 6
  • PageNo: 1707-1712
  • Abstract:
  • In this research, we offer an enhanced deep learning technique for recognition of sign language. We offer a unique architecture that efficiently captures the spatial and temporal properties of sign language motions by combining recurrent neural networks (RNN) with convolutional neural networks (CNN). signal. Our strategy achieves better accuracy and real-time performance than previous approaches.

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{169402,
        author = {Kartik Begani and Puneet Kaur and Tanu Pal and Anshika Gupta and Yash Gupta and Himani Chauhan},
        title = {real time Hand Gesture Recognition},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {1707-1712},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169402},
        abstract = {In this research, we offer an enhanced deep learning technique for recognition of sign language. We offer a unique architecture that efficiently captures the spatial and temporal properties of sign language motions by combining recurrent neural networks (RNN) with convolutional neural networks (CNN). signal. Our strategy achieves better accuracy and real-time performance than previous approaches.},
        keywords = {Sign language recognition, deep learning, convolutional neural networks (CNN), computer vision, machine learning.},
        month = {November},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 6
  • PageNo: 1707-1712

real time Hand Gesture Recognition

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