ICDAR2017 Robust Reading Challenge on COCO-Text

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Standard

ICDAR2017 Robust Reading Challenge on COCO-Text. / Gomez, Raul; Shi, Baoguang; Gomez, Lluis; Numann, Lukas; Veit, Andreas; Matas, Jiri; Belongie, Serge; Karatzas, Dismosthenis.

I: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 25.01.2018, s. 1435-1443.

Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt

Harvard

Gomez, R, Shi, B, Gomez, L, Numann, L, Veit, A, Matas, J, Belongie, S & Karatzas, D 2018, 'ICDAR2017 Robust Reading Challenge on COCO-Text', Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, s. 1435-1443. https://doi.org/10.1109/ICDAR.2017.234

APA

Gomez, R., Shi, B., Gomez, L., Numann, L., Veit, A., Matas, J., Belongie, S., & Karatzas, D. (2018). ICDAR2017 Robust Reading Challenge on COCO-Text. Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 1435-1443. https://doi.org/10.1109/ICDAR.2017.234

Vancouver

Gomez R, Shi B, Gomez L, Numann L, Veit A, Matas J o.a. ICDAR2017 Robust Reading Challenge on COCO-Text. Proceedings of the International Conference on Document Analysis and Recognition, ICDAR. 2018 jan. 25;1435-1443. https://doi.org/10.1109/ICDAR.2017.234

Author

Gomez, Raul ; Shi, Baoguang ; Gomez, Lluis ; Numann, Lukas ; Veit, Andreas ; Matas, Jiri ; Belongie, Serge ; Karatzas, Dismosthenis. / ICDAR2017 Robust Reading Challenge on COCO-Text. I: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR. 2018 ; s. 1435-1443.

Bibtex

@inproceedings{2d3f8844653e43cb9637e197c0f6d0b1,
title = "ICDAR2017 Robust Reading Challenge on COCO-Text",
abstract = "This report presents the final results of the ICDAR 2017 Robust Reading Challenge on COCO-Text. A challenge on scene text detection and recognition based on the largest real scene text dataset currently available: the COCO-Text dataset. The competition is structured around three tasks: Text Localization, Cropped Word Recognition and End-To-End Recognition. The competition received a total of 27 submissions over the different opened tasks. This report describes the datasets and the ground truth, details the performance evaluation protocols used and presents the final results along with a brief summary of the participating methods.",
author = "Raul Gomez and Baoguang Shi and Lluis Gomez and Lukas Numann and Andreas Veit and Jiri Matas and Serge Belongie and Dismosthenis Karatzas",
note = "Funding Information: *This work was supported by the Spanish project TIN2014-52072-P and the CERCA programme/Generalitat de Catalunya 1 R. Gomez is with the Computer Vision Center, Universitat Autonoma de Barcelona and Eurecat. 2 B. Shi is with the School of EIC, Huazhong University of Science and Technology 3,8 L. Gomez and D. Karatzas are with the Computer Vision Center, Universitat Autonoma de Barcelona. 4,6 L. Neumann and J. Matas are with the Center for Machine Perception, Czech Technical University. 5,7 A. Veit and S.Belongie are with the Cornell University and Cornell Tech. Publisher Copyright: {\textcopyright} 2017 IEEE.; 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017 ; Conference date: 09-11-2017 Through 15-11-2017",
year = "2018",
month = jan,
day = "25",
doi = "10.1109/ICDAR.2017.234",
language = "English",
pages = "1435--1443",
journal = "Proceedings of the International Conference on Document Analysis and Recognition, ICDAR",
issn = "1520-5363",

}

RIS

TY - GEN

T1 - ICDAR2017 Robust Reading Challenge on COCO-Text

AU - Gomez, Raul

AU - Shi, Baoguang

AU - Gomez, Lluis

AU - Numann, Lukas

AU - Veit, Andreas

AU - Matas, Jiri

AU - Belongie, Serge

AU - Karatzas, Dismosthenis

N1 - Funding Information: *This work was supported by the Spanish project TIN2014-52072-P and the CERCA programme/Generalitat de Catalunya 1 R. Gomez is with the Computer Vision Center, Universitat Autonoma de Barcelona and Eurecat. 2 B. Shi is with the School of EIC, Huazhong University of Science and Technology 3,8 L. Gomez and D. Karatzas are with the Computer Vision Center, Universitat Autonoma de Barcelona. 4,6 L. Neumann and J. Matas are with the Center for Machine Perception, Czech Technical University. 5,7 A. Veit and S.Belongie are with the Cornell University and Cornell Tech. Publisher Copyright: © 2017 IEEE.

PY - 2018/1/25

Y1 - 2018/1/25

N2 - This report presents the final results of the ICDAR 2017 Robust Reading Challenge on COCO-Text. A challenge on scene text detection and recognition based on the largest real scene text dataset currently available: the COCO-Text dataset. The competition is structured around three tasks: Text Localization, Cropped Word Recognition and End-To-End Recognition. The competition received a total of 27 submissions over the different opened tasks. This report describes the datasets and the ground truth, details the performance evaluation protocols used and presents the final results along with a brief summary of the participating methods.

AB - This report presents the final results of the ICDAR 2017 Robust Reading Challenge on COCO-Text. A challenge on scene text detection and recognition based on the largest real scene text dataset currently available: the COCO-Text dataset. The competition is structured around three tasks: Text Localization, Cropped Word Recognition and End-To-End Recognition. The competition received a total of 27 submissions over the different opened tasks. This report describes the datasets and the ground truth, details the performance evaluation protocols used and presents the final results along with a brief summary of the participating methods.

UR - http://www.scopus.com/inward/record.url?scp=85045206830&partnerID=8YFLogxK

U2 - 10.1109/ICDAR.2017.234

DO - 10.1109/ICDAR.2017.234

M3 - Conference article

AN - SCOPUS:85045206830

SP - 1435

EP - 1443

JO - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

JF - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

SN - 1520-5363

T2 - 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017

Y2 - 9 November 2017 through 15 November 2017

ER -

ID: 301826271