Convolution Neural Network based Hand Gesture Recognition System

Strong hand gesture recognition has been essential in the area of human-computer interaction for a very long time. Due of their intricacy and breadth, these gestures are difficult for many people to understand, which hinders communication between those with and without speech impairments. There is a lot of active practical research being done in the field of computer vision because of the current boom in deep learning. Till the date, various image processing algorithms have used color and depth cameras to recognize hand gestures, however it is still difficult to classify movements from different subjects accurately. The objective of this study is to identify hand gestures by using a camera to quickly follow the region of interest (ROI), which in this case is the hand region, in the image range. In this project, the use of convolutional neural networks (CNNs) in an algorithm for real-time hand gesture recognition has been proposed in this paper. On a dataset of thirty-six hand gestures and 400 photos for each gesture, the suggested CNN is anticipated to achieve excellent accuracy.