ICDAR2017 Robust Reading Challenge on COCO-Text
Research output: Contribution to journal › Conference article › Research › peer-review
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ICDAR2017 Robust Reading Challenge on COCO-Text. / Belongie, Serge; Gómez, Raúl; Shi, Baoguang; Gomez-Pujol, Lluis; Neumann, Lukas; Veit, Andreas; Matas, Jiri; Karatzas, Dimosthenis.
In: IEEE Xplore Digital Library, 29.01.2018.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - ICDAR2017 Robust Reading Challenge on COCO-Text
AU - Belongie, Serge
AU - Gómez, Raúl
AU - Shi, Baoguang
AU - Gomez-Pujol, Lluis
AU - Neumann, Lukas
AU - Veit, Andreas
AU - Matas, Jiri
AU - Karatzas, Dimosthenis
PY - 2018/1/29
Y1 - 2018/1/29
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.
U2 - 10.1109/ICDAR.2017.234
DO - 10.1109/ICDAR.2017.234
M3 - Conference article
JO - IEEE Xplore Digital Library
JF - IEEE Xplore Digital Library
ER -
ID: 307526853