Sign Language Recognition for Low Resource Languages Using Few Shot Learning

Sign Language Recognition (SLR) with machine learning is challenging due to the scarcity of data for most low-resource sign languages. Therefore, it is crucial to leverage a few-shot learning strategy for SLR. This research proposes a novel skeleton-based sign language recognition method based on the prototypical network [20] called ProtoSign. Furthermore, we contribute to the field by introducing the first publicly accessible dynamic word-level Sinhala Sign Language (SSL) video dataset comprising 1110 videos over 50 classes. To our knowledge, this is the first publicly available SSL dataset. Our method is evaluated using two low-resource language datasets, including our dataset. The experiments show the results in 95% confidence level for both 5-way and 10-way in 1-shot, 2-shot, and 5-shot settings.