Improving Handshape Representations for Sign Language Processing: A Graph Neural Network Approach
This addresses a bottleneck in sign language processing for improved recognition and linguistic analysis, though it is incremental as it builds on existing graph neural network methods.
The paper tackled the problem of handshape recognition in sign language processing by introducing a graph neural network that separates temporal dynamics from static configurations, achieving 46% accuracy across 37 classes compared to a 25% baseline.
Handshapes serve a fundamental phonological role in signed languages, with American Sign Language employing approximately 50 distinct shapes. However,computational approaches rarely model handshapes explicitly, limiting both recognition accuracy and linguistic analysis.We introduce a novel graph neural network that separates temporal dynamics from static handshape configurations. Our approach combines anatomically-informed graph structures with contrastive learning to address key challenges in handshape recognition, including subtle interclass distinctions and temporal variations. We establish the first benchmark for structured handshape recognition in signing sequences, achieving 46% accuracy across 37 handshape classes (with baseline methods achieving 25%).