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Authors: Hussein Chaaban ; Michèle Gouiffès and Annelies Braffort

Affiliation: Université Paris-Saclay, CNRS, LIMSI, 91400, Orsay, France

Keyword(s): LSF Videos, Annotations, Lexical Signs, Sign Segmentation.

Abstract: The automatic recognition of Sign Languages is the main focus of most of the works in the field, which explains the progressing demand on the annotated data to train the dedicated models. In this paper, we present a semi automatic annotation system for Sign Languages. Such automation will not only help to create training data but it will reduce as well the processing time and the subjectivity of manual annotations done by linguists in order to study the sign language. The system analyses hand shapes, hands speed variations, and face landmarks to annotate base level features and to separate the different signs. In a second stage, signs are classified into two types, whether they are lexical (i.e. present in a dictionary) or iconic (illustrative), using a probabilistic model. The results show that our system is partially capable of annotating automatically the video sequence with a F1 score = 0.68 for lexical sign annotation and an error of 3.8 frames for sign segmentation. An expert v alidation of the annotations is still needed. (More)

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Paper citation in several formats:
Chaaban, H.; Gouiffès, M. and Braffort, A. (2021). Automatic Annotation and Segmentation of Sign Language Videos: Base-level Features and Lexical Signs Classification. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP; ISBN 978-989-758-488-6; ISSN 2184-4321, SciTePress, pages 484-491. DOI: 10.5220/0010247104840491

@conference{visapp21,
author={Hussein Chaaban. and Michèle Gouiffès. and Annelies Braffort.},
title={Automatic Annotation and Segmentation of Sign Language Videos: Base-level Features and Lexical Signs Classification},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP},
year={2021},
pages={484-491},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010247104840491},
isbn={978-989-758-488-6},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP
TI - Automatic Annotation and Segmentation of Sign Language Videos: Base-level Features and Lexical Signs Classification
SN - 978-989-758-488-6
IS - 2184-4321
AU - Chaaban, H.
AU - Gouiffès, M.
AU - Braffort, A.
PY - 2021
SP - 484
EP - 491
DO - 10.5220/0010247104840491
PB - SciTePress