BosphorusSign22k Sign Language Recognition Dataset

Oğulcan Özdemir, Ahmet Alp Kındıroğlu, Necati Cihan Camgöz, Lale Akarun


Abstract
Sign Language Recognition is a challenging research domain. It has recently seen several advancements with the increased availability of data. In this paper, we introduce the BosphorusSign22k, a publicly available large scale sign language dataset aimed at computer vision, video recognition and deep learning research communities. The primary objective of this dataset is to serve as a new benchmark in Turkish Sign Language Recognition for its vast lexicon, the high number of repetitions by native signers, high recording quality, and the unique syntactic properties of the signs it encompasses. We also provide state-of-the-art human pose estimates to encourage other tasks such as Sign Language Production. We survey other publicly available datasets and expand on how BosphorusSign22k can contribute to future research that is being made possible through the widespread availability of similar Sign Language resources. We have conducted extensive experiments and present baseline results to underpin future research on our dataset.
Anthology ID:
2020.signlang-1.30
Volume:
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
Editors:
Eleni Efthimiou, Stavroula-Evita Fotinea, Thomas Hanke, Julie A. Hochgesang, Jette Kristoffersen, Johanna Mesch
Venue:
SignLang
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
181–188
Language:
English
URL:
https://aclanthology.org/2020.signlang-1.30
DOI:
Bibkey:
Cite (ACL):
Oğulcan Özdemir, Ahmet Alp Kındıroğlu, Necati Cihan Camgöz, and Lale Akarun. 2020. BosphorusSign22k Sign Language Recognition Dataset. 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 181–188, Marseille, France. European Language Resources Association (ELRA).
Cite (Informal):
BosphorusSign22k Sign Language Recognition Dataset (Özdemir et al., SignLang 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.signlang-1.30.pdf
Data
BosphorusSign22k