A Survey on Conversion of Audio and Text to Sign Language and vice versa using Deep learning

Millions of individuals throughout the world use sign language as their primary form of communication, particularly those who are deaf or hard of hearing. But because not everyone is proficient in sign language, it might be difficult to communicate and restrict access to information for sign language users. The communication gap between the users and non-users of sign language can be significantly closed by systems that can translate sign language into text and audio as well as audio into sign language. Deep learning models have been extremely effective in advancing the recognition and conversion of sign language during the past few decades. These tools translate sign language motions into written or spoken words by using computer vision and natural language processing techniques, and vice versa. But there are still many problems that need to be solved, including the need for more precise and durable deep learning models and the diversity of sign language dialects and cultural variations. The goal of this survey article is to present a summary of the state of the art in sign language conversion technologies, including deep learning models for sign language recognition, text and audio conversion from sign language, and audio to sign language conversion.