@inproceedings{koulierakis-etal-2020-recognition,
title = "Recognition of Static Features in Sign Language Using Key-Points",
author = "Koulierakis, Ioannis and
Siolas, Georgios and
Efthimiou, Eleni and
Fotinea, Evita and
Stafylopatis, Andreas-Georgios",
editor = "Efthimiou, Eleni and
Fotinea, Stavroula-Evita and
Hanke, Thomas and
Hochgesang, Julie A. and
Kristoffersen, Jette and
Mesch, Johanna",
booktitle = "Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/2020.signlang-1.20",
pages = "123--126",
abstract = "In this paper we report on a research effort focusing on recognition of static features of sign formation in single sign videos. Three sequential models have been developed for handshape, palm orientation and location of sign formation respectively, which make use of key-points extracted via OpenPose software. The models have been applied to a Danish and a Greek Sign Language dataset, providing results around 96{\%}. Moreover, during the reported research, a method has been developed for identifying the time-frame of real signing in the video, which allows to ignore transition frames during sign recognition processing.",
language = "English",
ISBN = "979-10-95546-54-2",
}
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<abstract>In this paper we report on a research effort focusing on recognition of static features of sign formation in single sign videos. Three sequential models have been developed for handshape, palm orientation and location of sign formation respectively, which make use of key-points extracted via OpenPose software. The models have been applied to a Danish and a Greek Sign Language dataset, providing results around 96%. Moreover, during the reported research, a method has been developed for identifying the time-frame of real signing in the video, which allows to ignore transition frames during sign recognition processing.</abstract>
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%0 Conference Proceedings
%T Recognition of Static Features in Sign Language Using Key-Points
%A Koulierakis, Ioannis
%A Siolas, Georgios
%A Efthimiou, Eleni
%A Fotinea, Evita
%A Stafylopatis, Andreas-Georgios
%Y Efthimiou, Eleni
%Y Fotinea, Stavroula-Evita
%Y Hanke, Thomas
%Y Hochgesang, Julie A.
%Y Kristoffersen, Jette
%Y Mesch, Johanna
%S Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives
%D 2020
%8 May
%I European Language Resources Association (ELRA)
%C Marseille, France
%@ 979-10-95546-54-2
%G English
%F koulierakis-etal-2020-recognition
%X In this paper we report on a research effort focusing on recognition of static features of sign formation in single sign videos. Three sequential models have been developed for handshape, palm orientation and location of sign formation respectively, which make use of key-points extracted via OpenPose software. The models have been applied to a Danish and a Greek Sign Language dataset, providing results around 96%. Moreover, during the reported research, a method has been developed for identifying the time-frame of real signing in the video, which allows to ignore transition frames during sign recognition processing.
%U https://aclanthology.org/2020.signlang-1.20
%P 123-126
Markdown (Informal)
[Recognition of Static Features in Sign Language Using Key-Points](https://aclanthology.org/2020.signlang-1.20) (Koulierakis et al., SignLang 2020)
ACL
- Ioannis Koulierakis, Georgios Siolas, Eleni Efthimiou, Evita Fotinea, and Andreas-Georgios Stafylopatis. 2020. Recognition of Static Features in Sign Language Using Key-Points. In Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives, pages 123–126, Marseille, France. European Language Resources Association (ELRA).