NLP-based Sign Gesture Identification for Disabled People

Abstract

There are many methods for identifying  signs, each of which generates a word for each one. It  focuses on converting sign language into an appropriate  English sentence. NLP techniques are also used in addition  to sign recognition. The input is a framed and split video of  sign language. This booklet teaches deaf and mute people  sign language. It's tough for non-blind persons to engage  with blind people due to communication difficulties. To  address this issue, the article suggests and describes an  effective method. Language technology methods such as  POS tagging and the LALR parser are used to convert  identified sign words into English phrases. A number of  applications are currently on the industry that allow blind  people to interact with the world. Combining technology  will not be able to address the problem of mobile sign  language translation in daily activities. A video interpreter  can assist deaf or hearing-impaired people in a variety of  situations. People with hearing impairments will be able to  learn sign language and have films translated into sign  language as a consequence of this research. The present  work may be used as a communication interface for both  speech-impaired and non-speech-impaired individuals. It  will assist bridge the communication gap between speech impaired people and the rest of the population by capturing  and analyzing signals, as well as recognizing and displaying  output in the form of comprehensible phrases.