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.