Informatica

Limited proficiency in sign language creates communication barriers, motivating the development of robust Automatic Sign Language Recognition (SLR) systems. We address isolated SLR in a low-resource setting using few-shot metric-based meta-learning. Sign videos are encoded with spatiotemporal convolutional backbones and classified using a prototypical network, enabling generalization to unseen classes from small support sets. We compare the SlowFast architecture with state-of-the-art video models on the LSA64 benchmark under strict class-disjoint protocols. SlowFast achieves 94.33% accuracy, outperforming competing backbones and demonstrating an effective and data-efficient approach for low-resource isolated SLR.