Handwave Harmony: Transforminggestures into Words

https://doi.org/10.55529/jaimlnn.45.1.6

Authors

  • Mr. M. V. Subba Rao Assistant Professor, Department of Information Technology, India.
  • K. T. V. Narasimhaswamy Department of Computer Science and Business Systems, Vishnu Institute of Technology, Andhra Pradesh, India.
  • A. Boni Supriya Department of Computer Science and Business Systems, Vishnu Institute of Technology, Andhra Pradesh, India.
  • M. M. S. N. Deekshitha Department of Computer Science and Business Systems, Vishnu Institute of Technology, Andhra Pradesh, India.

Keywords:

Gesture Recognition, Real-Time Systems, Assistive Technologies, Deep Learning, Convolutional Neural Networks (Cnns), Resnet50.

Abstract

Our project introduces a real-time gesture recognition system using ResNet50 and OpenCV. This innovative technology offers precise interpretation of sign movements for individuals with hearing difficulties. This represents a form of interpersonal connection where individuals convey understanding, empathy, or agreement through the subtle movements of their hands. The system is trained on a diverse dataset for flexibility and utilizes ResNet50's capabilities while integrating OpenCV for reduced latency, ensuring effective communication. Rigorous testing validates its accuracy, with user input fosters iterative learning. The study highlights the Importance of continuous enhancement and collaboration with the deaf and hard-of-hearing community for greater inclusiveness.

Published

2024-08-01

How to Cite

Mr. M. V. Subba Rao, K. T. V. Narasimhaswamy, A. Boni Supriya, & M. M. S. N. Deekshitha. (2024). Handwave Harmony: Transforminggestures into Words. Journal of Artificial Intelligence,Machine Learning and Neural Network , 4(5), 1–6. https://doi.org/10.55529/jaimlnn.45.1.6

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