@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.html},
  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.}
}

@inproceedings{camgoz-etal-2016-bosphorussign:lrec,
  author    = {Camg{\"o}z, Necati Cihan and K{\i}nd{\i}ro{\u g}lu, Ahmet Alp and Karab{\"u}kl{\"u}, Serpil and Kelepir, Meltem and {\"O}zsoy, Ay{\c s}e Sumru and Akarun, Lale},
  title     = {{B}osphorus{S}ign: A {T}urkish {S}ign {L}anguage Recognition Corpus in Health and Finance Domains},
  pages     = {1383--1388},
  editor    = {Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Goggi, Sara and Grobelnik, Marko and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios},
  booktitle = {10th International Conference on Language Resources and Evaluation ({LREC} 2016)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Portoro{\v z}, Slovenia},
  day       = {23--28},
  month     = may,
  year      = {2016},
  isbn      = {978-2-9517408-9-1},
  language  = {english},
  url       = {https://aclanthology.org/L16-1220},
  abstract  = {There are as many sign languages as there are deaf communities in the world. Linguists have been collecting corpora of different sign languages and annotating them extensively in order to study and understand their properties. On the other hand, the field of computer vision has approached the sign language recognition problem as a grand challenge and research efforts have intensified in the last 20 years. However, corpora collected for studying linguistic properties are often not suitable for sign language recognition as the statistical methods used in the field require large amounts of data. Recently, with the availability of inexpensive depth cameras, groups from the computer vision community have started collecting corpora with large number of repetitions for sign language recognition research. In this paper, we present the BosphorusSign Turkish Sign Language corpus, which consists of 855 sign and phrase samples from the health, finance and everyday life domains. The corpus is collected using the state-of-the-art Microsoft Kinect v2 depth sensor, and will be the first in this sign language research field. Furthermore, there will be annotations rendered by linguists so that the corpus will appeal both to the linguistic and sign language recognition research communities.}
}