Hand Gesture Recognition using Machine Learning and OpenCV

Authors

  • Girish L. Head and Professor, Department of Computer Science and Engineering, Shridevi Institute of Engineering and Technology, Tumakuru, Karnataka, India
  • Harshitha C. Student, Department of Computer Science and Engineering, Shridevi Institute of Engineering and Technology, Tumakuru, Karnataka, India
  • Chethan V. Student, Department of Computer Science and Engineering, Shridevi Institute of Engineering and Technology, Tumakuru, Karnataka, India
  • J.N. Shreyas Student, Department of Computer Science and Engineering, Shridevi Institute of Engineering and Technology, Tumakuru, Karnataka, India
  • Bhavana C. Student, Department of Computer Science and Engineering, Shridevi Institute of Engineering and Technology, Tumakuru, Karnataka, India

Keywords:

Image processing, Machine Learning, OpenCV, sign language recognition

Abstract

People who are deaf or dumb use sign language to communicate with one another and within their own communities. The process of computer recognition of sign language begins with learning sign gestures and progresses until text or speech is generated. Static and dynamic sign gestures are the two categories. Although static gesture recognition is simpler to use than dynamic gesture recognition, both gesture recognition systems are essential to the survival of the human species. The processes for sign language recognition are described in this survey. Data gathering, preprocessing, transformation, feature extraction, classification, and results are all examined. Additionally, there were some suggestions for developing this area of work.

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Published

2023-11-23

Issue

Vol. 10 No. 3 (2023): Journal of Image Processing & Pattern Recognition Progress

Section

Review Article