Abstract:
Human interaction with computers and other machines is becoming an
increasingly important and relevant topic in the modern world. Hand gesture
recognition technology is an innovative approach to managing computers
and electronic devices that allows users to interact with technology through
gestures and hand movements. This article presents deep learning methods
that allow you to efficiently process and classify hand gestures and hand
gesture recognition technologies for interacting with computers. This paper
discusses modern deep learning methods such as convolutional neural
networks (CNN) and recurrent neural networks (RNN), which show
excellent results in gesture recognition tasks. Next, the development and
implementation of a human-machine interaction system based on hand
gesture recognition is discussed. System architectures are described, as well
as technical and practical aspects of their application. In conclusion, the
article summarizes the research results and outlines the prospects for the
development of hand gesture recognition technology to improve humanmachine interaction. The advantages and limitations of the technology are
analyzed, as well as possible areas of its application in the future.
