Connecting the Dots: Leveraging Spatio-Temporal Graph Neural Networks
  for Accurate Bangla Sign Language Recognition
Haz Sameen Shahgir Khondker Salman Sayeed Md Toki Tahmid Tanjeem Azwad Zaman Md. Zarif Ul Alam
Recent advances in Deep Learning and Computer Vision have been successfully
leveraged to serve marginalized communities in various contexts. One such area
is Sign Language - a primary means of communication for the deaf community.
However, so far, the bulk of research efforts and investments have gone into
American Sign Language, and research activity into low-resource sign languages
- especially Bangla Sign Language - has lagged significantly. In this research
paper, 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 Graph
Neural Network approach for the classification of BdSL40 dataset. This is the
first study on word-level BdSL recognition, and the dataset was transcribed
from Indian Sign Language (ISL) using the Bangla Sign Language Dictionary
(1997). The proposed GNN model achieved an F1 score of 89%. The study
highlights the significant lexical and semantic similarity between BdSL, West
Bengal Sign Language, and ISL, and the lack of word-level datasets for BdSL in
the literature. We release the dataset and source code to stimulate further
research.