Hand Recognition and Gesture Control using Neural Networks

  • Senthil Kumar G, Sampath T, Mohan Ram M, Muthu Veerapan N

Abstract

In the modern era, lots of things are getting automated and everything is getting digitized. The daily interaction between computers and humans is essential. The development in the interaction between humans and computers is being limited as we need a special type of physical equipment to trigger a command to start a process. To make this procedure increasingly intelligent, the signal acknowledgment has become a vital issue for investigation in the field of PC vision, where PCs can be made to increase a significant level of comprehension from computerized pictures or recordings. A lot of methods have been proposed for recognizing and processing the gesture movement using various sensors and neural networks. In this project, we provide a way for recognizing the hand gesture and processing it using computer vision. The proposed framework utilizes the webcam of the PC framework to take the hand motions as info and the cursor reacts as needs. It can be applied to control electronic appliances and it also uses an optical recognition system that recognizes gestures in different light conditions. The various gestures performed by the user are trained at first to avoid the miscalculation and basically, gestures are being interpreted by mathematical algorithms. Various gestures performed by the user such as rotating clockwise and counter-clockwise, pushing, holding and swiping and with the help of gestures we can also write an alphabet. The gesture processing can be applied to various fields like augmented reality and virtual reality and it's also applicable in the medical as well as engineering field.

Published
2020-04-18
How to Cite
Senthil Kumar G, Sampath T, Mohan Ram M, Muthu Veerapan N. (2020). Hand Recognition and Gesture Control using Neural Networks. International Journal of Control and Automation, 13(02), 459 - 467. Retrieved from https://sersc.org/journals/index.php/IJCA/article/view/10174
Section
Articles