Word level Bangla Sign Language Dataset for Continuous BSL Recognition

Abstract: 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%.