Sign Language Recognition Based on Hands Symbol's Classification

Abstract

One of the most natural and ancient types of conversational language is sign language. The technology that converts sign language into writing for people who have difficulty communicating, such as those who have speech issues, hearing disabilities, or are deaf, is the subject of this study. This paper is based on a real-time method based on finger writing and neural networks for American sign language. An interesting field of vision study is the automatic recognition of human gestures from video images. The Research recommend employing a convolution neural network (CNN) method to recognize human hand motions from a photograph. The objective is to identify hand movements used in human work activities from a camera image. Hand placement and orientation are employed to collect the training and assessment data for CNN. The hand is first put through a filter, and once that has been done, it is put through a classification, which determines what class the hand movements belong to. Then, CNN is trained using the measured pictures.