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Recent advances in Deep Learning and Computer Vision have been successfullyleveraged to serve marginalized communities in various contexts. One such areais Sign Language - a primary means of communication for the deaf community.However, so far, the bulk of research efforts and investments have gone intoAmerican Sign Language, and research activity into low-resource sign languages- especially Bangla Sign Language - has lagged significantly. In this researchpaper, we present a new word-level Bangla Sign Language dataset - BdSL40 -consisting of 611 videos over 40 words, along with two different approaches:one with a 3D Convolutional Neural Network model and another with a novel GraphNeural Network approach for the classification of BdSL40 dataset. This is thefirst study on word-level BdSL recognition, and the dataset was transcribedfrom Indian Sign Language (ISL) using the Bangla Sign Language Dictionary(1997). The proposed GNN model achieved an F1 score of 89%. The studyhighlights the significant lexical and semantic similarity between BdSL, WestBengal Sign Language, and ISL, and the lack of word-level datasets for BdSL inthe literature. We release the dataset and source code to stimulate furtherresearch.

Connecting the Dots: Leveraging Spatio-Temporal Graph Neural Networks for Accurate Bangla Sign Language Recognition