Journal of Control & Instrumentation

Journal of Control & Instrumentation Original Research

Implementation of Human Gesture Recognition Using CNN

  1. A. A. Hakim SKN Sinhgad College of Engineering, Korti, Pandharpur,,
  2. A. D. Harale SKN Sinhgad College of Engineering, Korti, Pandharpur,,
  3. A. O. Mulani SKN Sinhgad College of Engineering, Korti, Pandharpur,,
  4. K. J. Karande SKN Sinhgad College of Engineering, Korti, Pandharpur,,

Abstract

A gesture popularity system based entirely on convolutional neural networks (CNNs). Preprocessing techniques include segmentation, polygonal approximation, contour construction, morphological filters, and resource characteristic extraction. Various convolutional neural networks are employed for training and testing, with results compared to existing architectures and protocols. All generated measurements and convergence graphs produced at any point during education are examined and contested in order to verify the reliability of the approach offered. Our project was created to record hand gestures as we entered and predict textual signal languages. It makes use of the Raspberry Pi module, which is among the best for editing photos and filming videos.

Keywords

  • Feature Extraction
  • Hand Gesture
  • Raspberry Pi
  • open CV
  • python

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