Sign Language Recognition - Satwik Ram Kodandaram, N Pavan Kumar, Sunil G L, (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Sign Language is mainly used by deaf (hard hearing) and dumbpeople to exchange information between their own community and with other people. It is a language where people use theirhand gestures to communicate as they can’t speak or hear. Sign Language Recognition (SLR) deals with recognizing the hand gestures acquisition and continues till text or speech is generated for corresponding hand gestures. Here hand gestures for sign language can be classified as static and dynamic.However, static hand gesture recognition is simpler than dynamic hand gesture recognition, but both recognition is important to the human community.We can use Deep Learning Computer Vision to recognize the hand gestures by building Deep Neural Network architectures (Convolution Neural Network Architectures) where the model will learn to recognize the hand gestures images over an epoch.Once the model Successfully recognizes the gesture the corresponding English text is generated and then text can be converted to speech.This model will be more efficient and hence communicate for the deaf (hard hearing) and dump people will beeasier. In this paper, we will discuss how Sign Language Recognition is done using Deep Learning