LSFB-CONT and LSFB-ISOL: Two New Datasets for Vision-Based Sign Language Recognition

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférenceArticle dans les actes d'une conférence/un colloque

133 Téléchargements (Pure)


While significant progress have been made in thefield of Natural Language Processing (NLP), leading the com-mercially available products, Sign Language Recognition (SLR)is still in its infancy. The lack of large-scale sign languagedatasets makes it hard to leverage new Deep Learning methods.In this paper, we introduce LSFB-CONT, a large scale datasetsuited for continuous SLR along with LSFB-ISOL, a subset ofLSFB-CONT for isolated SLR. Baseline SLR experiments areconducted on LSFB-ISOL and the reached accuracy measuresare compared with those obtained on previous datasets. Theresults suggest that state-of-the-art models for action recognitionstill lack sufficient internal representation power to capture thehigh level of variations of a sign language.
langue originaleAnglais
titreProceedings of the 2021 International Joint Conference on Neural Networks (IJCNN 2021)
EditeurIEEE Computer Society Press
Etat de la publicationPublié - 2021

Empreinte digitale

Examiner les sujets de recherche de « LSFB-CONT and LSFB-ISOL: Two New Datasets for Vision-Based Sign Language Recognition ». Ensemble, ils forment une empreinte digitale unique.

Contient cette citation