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Xydopoulos, and Vassia Zacharopoulou, from the department of Philology of the University of Patras, who provided their meaningful insights and expertise that greatly assisted this research. We would also like to express our gratitude to the Greek sign language center for their valuable feedback and contribution on the Greek sign language data capture.", "type": { "id": "notes", "title": { "de": "Anmerkungen", "en": "Notes" } } } ], "creators": [ { "affiliations": [ { "name": "Visual Computing Lab, Information Technologies Institute" } ], "person_or_org": { "family_name": "Ilias Papastratis", "identifiers": [ { "identifier": "0000-0003-4664-2626", "scheme": "orcid" } ], "name": "Ilias Papastratis", "type": "personal" } }, { "affiliations": [ { "name": "Visual Computing Lab, Information Technologies Institute" } ], "person_or_org": { "family_name": "Nikolas Adaloglou", "name": "Nikolas Adaloglou", "type": "personal" } }, { "affiliations": [ { "name": "Visual Computing Lab, Information Technologies Institute" } ], "person_or_org": { "family_name": "Andreas Stergioulas", "name": "Andreas Stergioulas", "type": "personal" } }, { "affiliations": [ { "name": "Visual Computing Lab, Information Technologies Institute" } ], "person_or_org": { "family_name": "Theocharis Chatzis", "name": "Theocharis Chatzis", "type": "personal" } }, { "affiliations": [ { "name": "Visual Computing Lab, Information Technologies Institute" } ], "person_or_org": { "family_name": "Georgios Th.Papadopoulos", "name": "Georgios Th.Papadopoulos", "type": "personal" } }, { "affiliations": [ { "name": "University of Patras" } ], "person_or_org": { "family_name": "Vassia Zacharopoulou", "name": "Vassia Zacharopoulou", "type": "personal" } }, { "affiliations": [ { "name": "University of Patras" } ], "person_or_org": { "family_name": "George J. Xydopoulos", "name": "George J. Xydopoulos", "type": "personal" } }, { "affiliations": [ { "name": "University of Patras" } ], "person_or_org": { "family_name": "Klimnis Atzakas", "name": "Klimnis Atzakas", "type": "personal" } }, { "affiliations": [ { "name": "University of Patras" } ], "person_or_org": { "family_name": "Dimitris Papazachariou", "name": "Dimitris Papazachariou", "type": "personal" } }, { "affiliations": [ { "name": "Visual Computing Lab, Information Technologies Institute" } ], "person_or_org": { "family_name": "Kosmas Dimitropoulos", "identifiers": [ { "identifier": "0000-0003-1584-7047", "scheme": "orcid" } ], "name": "Kosmas Dimitropoulos", "type": "personal" } }, { "affiliations": [ { "name": "Visual Computing Lab, Information Technologies Institute" } ], "person_or_org": { "family_name": "Petros Daras", "identifiers": [ { "identifier": "0000-0003-3814-6710", "scheme": "orcid" } ], "name": "Petros Daras", "type": "personal" } } ], "description": "
Abstract
\n\nThe Greek Sign Language (GSL) is a large-scale RGB+D dataset, suitable for Sign Language Recognition (SLR) and Sign Language Translation (SLT). The video captures are conducted using an Intel RealSense D435 RGB+D camera at a rate of 30 fps. Both the RGB and the depth streams are acquired in the same spatial resolution of 848×480 pixels. To increase variability in the videos, the camera position and orientation is slightly altered within subsequent recordings. Seven different signers are employed to perform 5 individual and commonly met scenarios in different public services. The average length of each scenario is twenty sentences.
\n\nDescription
\n\nThe dataset contains 10,290 sentence instances, 40,785 gloss instances, 310 unique glosses (vocabulary size) and 331 unique sentences, with 4.23 glosses per sentence on average. Each signer is asked to perform the pre-defined dialogues five consecutive times. In all cases, the simulation considers a deaf person communicating with a single public service employee. The involved signer performs the sequence of glosses of both agents in the discussion. For the annotation of each gloss sequence, GSL linguistic experts are involved. The given annotations are at individual gloss and gloss sequence level. A translation of the gloss sentences to spoken Greek is also provided.
\n\n\n\n
Evaluation
\n\nThe GSL dataset includes the 3 evaluation setups:
\n\n
\nEach zip file contains the videos for one scenario. Zip files named Depth contain the depth images of each video. The supplementary.zip contains the annotation files for the videos and the evaluation splits.
Citation
\n\nIf you use our dataset please cite our work :
\n\n@misc{adaloglou2021comprehensive,
\n title={A Comprehensive Study on Deep Learning-based Methods for Sign Language Recognition},
\n author={Nikolas Adaloglou and Theocharis Chatzis and Ilias Papastratis and Andreas Stergioulas and Georgios Th. Papadopoulos and Vassia Zacharopoulou and George J. Xydopoulos and Klimnis Atzakas and Dimitris Papazachariou and Petros Daras},
\n year={2021},
\n eprint={2007.12530},
\n archivePrefix={arXiv},
\n primaryClass={cs.CV}
\n}
\n\n
@ARTICLE{9393618,\n author={N. M. {Adaloglou} and T. {Chatzis} and I. {Papastratis} and A. {Stergioulas} and G. T. {Papadopoulos} and V. {Zacharopoulou} and G. {Xydopoulos} and K. {Antzakas} and D. {Papazachariou} and P. n. {Daras}},\n journal={IEEE Transactions on Multimedia}, \n title={A Comprehensive Study on Deep Learning-based Methods for Sign Language Recognition}, \n year={2021},\n volume={},\n number={},\n pages={1-1},\n doi={10.1109/TMM.2021.3070438}}\n", "languages": [ { "id": "gss", "title": { "en": "Greek Sign Language" } } ], "publication_date": "2020-07-14", "publisher": "Zenodo", "related_identifiers": [ { "identifier": "10.1109/ACCESS.2020.2993650", "relation_type": { "id": "issupplementto", "title": { "de": "Erg\u00e4nzt", "en": "Is supplement to" } }, "resource_type": { "id": "publication-article", "title": { "de": "Zeitschriftenartikel", "en": "Journal article" } }, "scheme": "doi" }, { "identifier": "https://arxiv.org/abs/2007.12530", "relation_type": { "id": "iscitedby", "title": { "de": "Wird zitiert von", "en": "Is cited by" } }, "resource_type": { "id": "publication-preprint", "title": { "de": "Preprint", "en": "Preprint" } }, "scheme": "url" }, { "identifier": "10.1109/TMM.2021.3070438", "relation_type": { "id": "iscitedby", "title": { "de": "Wird zitiert von", "en": "Is cited by" } }, "resource_type": { "id": "publication-article", "title": { "de": "Zeitschriftenartikel", "en": "Journal article" } }, "scheme": "doi" } ], "resource_type": { "id": "dataset", "title": { "de": "Datensatz", "en": "Dataset" } }, "rights": [ { "description": { "en": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited." }, "icon": "cc-by-icon", "id": "cc-by-4.0", "props": { "scheme": "spdx", "url": "https://creativecommons.org/licenses/by/4.0/legalcode" }, "title": { "en": "Creative Commons Attribution 4.0 International" } } ], "subjects": [ { "subject": "sign language recognition" }, { "subject": "sign language" }, { "subject": "deep learning" }, { "subject": "computer vision" }, { "subject": "dataset" } ], "title": "The Greek Sign Language (GSL) Dataset" }, "parent": { "access": { "owned_by": { "user": 112418 } }, "communities": {}, "id": "3941810", "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.3941810", "provider": "datacite" } } }, "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.3941811", "provider": "datacite" }, "oai": { "identifier": "oai:zenodo.org:3941811", "provider": "oai" } }, "revision_id": 15, "stats": { "all_versions": { "data_volume": 20873362554662.0, "downloads": 6256, "unique_downloads": 2419, "unique_views": 2999, "views": 3306 }, "this_version": { "data_volume": 16419199110841.0, "downloads": 5113, "unique_downloads": 1640, "unique_views": 2451, "views": 2707 } }, "status": "published", "updated": "2021-05-13T08:47:17.051524+00:00", "versions": { "index": 1, "is_latest": false } }