  A machine can understand human activities, and the meaning of signs can help
overcome the communication barriers between the inaudible and ordinary people.
Sign Language Recognition (SLR) is a fascinating research area and a crucial
task concerning computer vision and pattern recognition. Recently, SLR usage
has increased in many applications, but the environment, background image
resolution, modalities, and datasets affect the performance a lot. Many
researchers have been striving to carry out generic real-time SLR models. This
review paper facilitates a comprehensive overview of SLR and discusses the
needs, challenges, and problems associated with SLR. We study related works
about manual and non-manual, various modalities, and datasets. Research
progress and existing state-of-the-art SLR models over the past decade have
been reviewed. Finally, we find the research gap and limitations in this domain
and suggest future directions. This review paper will be helpful for readers
and researchers to get complete guidance about SLR and the progressive design
of the state-of-the-art SLR model
