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arXiv · 2504.08593

Hands-On: Segmenting Individual Signs from Continuous Sequences

JianHe Low·Harry Walsh·Ozge Mercanoglu Sincan·Richard Bowden
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Abstract

This work tackles the challenge of continuous sign language segmentation, a key task with huge implications for sign language translation and data annotation. We propose a transformer-based architecture that models the temporal dynamics of signing and frames segmentation as a sequence labeling problem using the Begin-In-Out (BIO) tagging scheme. Our method leverages the HaMeR hand features, and is complemented with 3D Angles. Extensive experiments show that our model achieves state-of-the-art results on the DGS Corpus, while our features surpass prior benchmarks on BSLCorpus.

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BibTeXRIS

JianHe Low, Harry Walsh, Ozge Mercanoglu Sincan, Richard Bowden. 2025-04-11. Hands-On: Segmenting Individual Signs from Continuous Sequences. https://doi.org/10.1109/fg61629.2025.11099255

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