[IPOL announce] new article: A Brief Review and Analysis of Two Methods for Automatic Sign Language Segmentation

[announcements about the IPOL journal]

A new article is available in IPOL: https://www.ipol.im/pub/art/2025/560/

Ariel E. Stassi, J. Matías Di Martino, and Gregory Randall,
A Brief Review and Analysis of Two Methods for Automatic Sign Language 
Segmentation,
Image Processing On Line, 15 (2025), pp. 59–77.
https://doi.org/10.5201/ipol.2025.560


Abstract
Sign language segmentation is a fundamental task in sign language 
processing to implement automatic translation systems. In this work, we 
study and compare the performance of two state-of-the-art methods for 
automatic sign language segmentation: 'Automatic Segmentation of Sign 
Language into Subtitle-Units' [Bull et al., European Conference on 
Computer Vision Workshops, 2020] and 'Linguistically Motivated Sign 
Language Segmentation' [Moryossef et al., Findings of the Association 
for Computational Linguistics, 2023]. Each method has an online demo 
available that can be used to run the approaches here presented on 
example videos, varying parameters such as considered pose models and 
probability thresholds. Both methods use pauses and movements of the 
derived skeletons to detect the limits of a phrase. We consider two 
datasets, one of American Sign Language (the test set of How2Sign) and 
one of Uruguayan Sign Language (LSU-DS). For the evaluation, we consider 
two metrics used in the paper of Moryossef et al. In the case of LSU-DS, 
as we have triplets of simultaneous videos taken from different points 
of view, we propose to use the IoU dispersion among points of view to 
estimate the coherence of the temporal segmentation of a unique signer 
simultaneously observed by different cameras. The performances of the 
different variants of each method are evaluated, showing the limits of 
the methods, the datasets, and the metrics to capture the quality of the 
automatic solutions.