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.
Keywords
Machine / Deep Learning – Machine Learning methods both in the visual domain and on linguistic annotation of sign language data
Machine / Deep Learning – Human-computer interfaces to sign language data and sign language annotation profiting from Machine Learning
@inproceedings{ozdemir:20005:sign-lang:lrec,
author = {{\"O}zdemir, O{\u g}ulcan and K{\i}nd{\i}ro{\u g}lu, Ahmet Alp and Camg{\"o}z, Necati Cihan and Akarun, Lale},
title = {{BosphorusSign22k} Sign Language Recognition Dataset},
pages = {181--188},
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},
maintitle = {12th International Conference on Language Resources and Evaluation ({LREC} 2020)},
publisher = {{European Language Resources Association (ELRA)}},
address = {Marseille, France},
day = {16},
month = may,
year = {2020},
isbn = {979-10-95546-54-2},
language = {english},
url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/20005.pdf}
}