  Sign language is a visual language that enhances communication between people
and is frequently used as the primary form of communication by people with
hearing loss. Even so, not many people with hearing loss use sign language, and
they frequently experience social isolation. Therefore, it is necessary to
create human-computer interface systems that can offer hearing-impaired people
a social platform. Most commercial sign language translation systems now on the
market are sensor-based, pricey, and challenging to use. Although vision-based
systems are desperately needed, they must first overcome several challenges.
Earlier continuous sign language recognition techniques used hidden Markov
models, which have a limited ability to include temporal information. To get
over these restrictions, several machine learning approaches are being applied
to transform hand and sign language motions into spoken or written language. In
this study, we compare various deep learning techniques for recognising sign
language. Our survey aims to provide a comprehensive overview of the most
recent approaches and challenges in this field.
