2026-07-28T23:28:10Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:14572 2024-03-06T10:31:28Z ddc:004 open_access

A Data Augmentation Approach for Sign-Language-To-Text Translation In-The-Wild Nunnari, Fabrizio España-Bonet, Cristina Avramidis, Eleftherios sing language video recognition end-to-end translation data augmentation In this paper, we describe the current main approaches to sign language translation which use deep neural networks with videos as input and text as output. We highlight that, under our point of view, their main weakness is the lack of generalization in daily life contexts. Our goal is to build a state-of-the-art system for the automatic interpretation of sign language in unpredictable video framing conditions. Our main contribution is the shift from image features to landmark positions in order to diminish the size of the input data and facilitate the combination of data augmentation techniques for landmarks. We describe the set of hypotheses to build such a system and the list of experiments that will lead us to their verification. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Fabrizio Nunnari and Cristina España-Bonet and Eleftherios Avramidis 2021 Is Part Of OASIcs, Volume 93, 3rd Conference on Language, Data and Knowledge (LDK 2021) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.LDK.2021.36 urn:nbn:de:0030-drops-145728 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.LDK.2021.36 eng https://creativecommons.org/licenses/by/4.0/legalcode