A Pattern Recognition Model: Hand Gestures Recognition using Convolutional Neural Networks

Human Beings have the natural ability to recognize body and sign language easily. This is possible because of vision and synaptic interactions formed during brain development. Humans also can pick up contextual information to understand body and sign language. In order to replicate this ability in computers, it is highly required to solve several issues-the ability to differentiate between objects of interest and the background. Choosing image capture technology and image classification algorithms etc. So, Hand gesture recognition is quickly becoming an important and relevant technology. Given the recent growth of AR and VR technologies, Hand gesture recognition has become an exciting new technology field. But currently, hand gestures are very specialized and haven’t acquired mainstream adoption. This study intends to show that gesture recognition can enhance many aspects of computer work and teaching. In our predicted model, the system works by the user starting the program, which would start providing the live webcam feed. The user then makes the hand gesture inside the frame of detection shown on the screen. When the hand gesture is made, the gesture is segmented and isolated. Some possible applications are to reduce the use of a physical keyboard and mouse by using hand gestures recognition to control computer operations like Pause/Play, close and open windows, and manipulate media controls. Another application is sign language. Hand gesture recognition can be used for communication and teaching purposes.