Published Paper Details:

The research analysis on sign language crosses multiple fields and academic areas. These days, the two primary fields of research in gesture recognition are data glove use and visual sign language recognition. While the latter records the user's hand features with the camera for the purpose of identifying and translating sign language, the former uses the information collected by the sensor for these purposes. Deaf and hard to hearing individuals typically employ sign language as a form to interact both within and outside of their own community. In this language, communication is facilitated through hand gestures, which is particularly essential for individuals who are deaf and mute. The goal of SLR is to recognize these hand signals and translate them into spoken or written language.Within this domain, hand signs are classified into types: dynamic and static. While recognizing static hand gestures is generally easier, the recognition of both dynamic and static gestures is valued by the community. Hand gestures could be recognized using Deep Learning Computer Vision and Deep Neural Network concepts (Convolution Neural Network designs). The model will learn to recognize the hand gesture photos over the course of an epoch.