  An robust sign language recognition system can greatly alleviate
communication barriers, particularly for people who struggle with verbal
communication. This is crucial for human growth and progress as it enables the
expression of thoughts, feelings, and ideas. However, sign recognition is a
complex task that faces numerous challenges such as same gesture patterns for
multiple signs, lighting, clothing, carrying conditions, and the presence of
large poses, as well as illumination discrepancies across different views.
Additionally, the absence of an extensive Bangla sign language video dataset
makes it even more challenging to operate recognition systems, particularly
when utilizing deep learning techniques. In order to address this issue,
firstly, we created a large-scale dataset called the MVBSL-W50, which comprises
50 isolated words across 13 categories. Secondly, we developed an
attention-based Bi-GRU model that captures the temporal dynamics of pose
information for individuals communicating through sign language. The proposed
model utilizes human pose information, which has shown to be successful in
analyzing sign language patterns. By focusing solely on movement information
and disregarding body appearance and environmental factors, the model is
simplified and can achieve a speedier performance. The accuracy of the model is
reported to be 85.64%.
