@inproceedings{mukushev-etal-2022-towards-semi,
title = "Towards Semi-automatic Sign Language Annotation Tool: {SLAN}-tool",
author = "Mukushev, Medet and
Sabyrov, Arman and
Sultanova, Madina and
Kimmelman, Vadim and
Sandygulova, Anara",
editor = "Efthimiou, Eleni and
Fotinea, Stavroula-Evita and
Hanke, Thomas and
Hochgesang, Julie A. and
Kristoffersen, Jette and
Mesch, Johanna and
Schulder, Marc",
booktitle = "Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.signlang-1.25",
pages = "159--164",
abstract = "This paper presents a semi-automatic annotation tool for sign languages namely SLAN-tool. The SLAN-tool provides a web-based service for the annotation of sign language videos. Researchers can use the SLAN-tool web service to annotate new and existing sign language datasets with different types of annotations, such as gloss, handshape configurations, and signing regions. This is allowed using a custom tier adding functionality. A unique feature of the tool is its automatic annotation functionality which uses several neural network models in order to recognize signing segments from videos and classify handshapes according to HamNoSys handshape inventory. Furthermore, SLAN-tool users can export annotations and import them into ELAN. The SLAN-tool is publicly available at \url{https://slan-tool.com}.",
}
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<abstract>This paper presents a semi-automatic annotation tool for sign languages namely SLAN-tool. The SLAN-tool provides a web-based service for the annotation of sign language videos. Researchers can use the SLAN-tool web service to annotate new and existing sign language datasets with different types of annotations, such as gloss, handshape configurations, and signing regions. This is allowed using a custom tier adding functionality. A unique feature of the tool is its automatic annotation functionality which uses several neural network models in order to recognize signing segments from videos and classify handshapes according to HamNoSys handshape inventory. Furthermore, SLAN-tool users can export annotations and import them into ELAN. The SLAN-tool is publicly available at https://slan-tool.com.</abstract>
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%0 Conference Proceedings
%T Towards Semi-automatic Sign Language Annotation Tool: SLAN-tool
%A Mukushev, Medet
%A Sabyrov, Arman
%A Sultanova, Madina
%A Kimmelman, Vadim
%A Sandygulova, Anara
%Y Efthimiou, Eleni
%Y Fotinea, Stavroula-Evita
%Y Hanke, Thomas
%Y Hochgesang, Julie A.
%Y Kristoffersen, Jette
%Y Mesch, Johanna
%Y Schulder, Marc
%S Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F mukushev-etal-2022-towards-semi
%X This paper presents a semi-automatic annotation tool for sign languages namely SLAN-tool. The SLAN-tool provides a web-based service for the annotation of sign language videos. Researchers can use the SLAN-tool web service to annotate new and existing sign language datasets with different types of annotations, such as gloss, handshape configurations, and signing regions. This is allowed using a custom tier adding functionality. A unique feature of the tool is its automatic annotation functionality which uses several neural network models in order to recognize signing segments from videos and classify handshapes according to HamNoSys handshape inventory. Furthermore, SLAN-tool users can export annotations and import them into ELAN. The SLAN-tool is publicly available at https://slan-tool.com.
%U https://aclanthology.org/2022.signlang-1.25
%P 159-164
Markdown (Informal)
[Towards Semi-automatic Sign Language Annotation Tool: SLAN-tool](https://aclanthology.org/2022.signlang-1.25) (Mukushev et al., SignLang 2022)
ACL
- Medet Mukushev, Arman Sabyrov, Madina Sultanova, Vadim Kimmelman, and Anara Sandygulova. 2022. Towards Semi-automatic Sign Language Annotation Tool: SLAN-tool. In Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources, pages 159–164, Marseille, France. European Language Resources Association.