Overview - ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text
This is a challenge of scene text understanding, which can be broken down into scene text detection, recognition, and spotting problems. The main novelty of this competition resides in the nature of the competition's dataset - the ArT dataset. Specifically, almost a quarter of the text instances in the dataset are arbitrary-shaped as illustrated in Figure 1, which are rarely seen in previous commonly used benchmarks [1, 2, 3]. There are three main tasks in this competition, which will are detailed in the Tasks tab.
Figure 1. Example images of the ArT dataset. Red color binding lines are formed with polygon ground truth format.
ArT is a combination of Total-Text , SCUT-CTW1500  and Baidu Curved Scene Text, which were collected with the motive of introducing the arbitrary-shaped text problem to the scene text community. On top of the existing images (3055), more than 7121 images are added to mixture of both datasets, which make ArT one of the larger scale scene text datasets today. There is a total of 10,176 images in the ArT dataset. It is split into a training set with 5603 images, and a testing set of 4573 newly collected images. The ArT dataset was collected with text shape diversity in mind, hence all existing text shapes (i.e. horizontal, multi-oriented, and curved) have high number of existence in the dataset, which makes it an unique dataset since most of the existing datasets [1, 2, 3] were dominated by horizontal and multi-oriented text instances only.
Text instances in the ArT dataset were annotated with (a) quadrilateral bounding boxes, 8, 10 and 12 vertexes polygon bounding box (more details in Tasks tab), and (b) transcription. Both of these annotations cater for the (a) text detection, (b) recognition, and (c) text spotting tasks proposed by this challenge.
The prize for ICDAR 2019-ArT is $8,700 in total, sponsored by Baidu.
Task 1. Scene Text Detection, $1,700/$800/$400 for top 3 winners.
Task 2. Scene Text Recognition, $1,700/$800/$400 for top 3 winners.
Task 3. Scene Text Spotting, $1,700/$800/$400 for top 3 winners.
- Karatzas, Dimosthenis, et al. "ICDAR 2013 robust reading competition." Document Analysis and Recognition (ICDAR), 2013 12th International Conference on. IEEE, 2013.
- Karatzas, Dimosthenis, et al. "ICDAR 2015 competition on robust reading." Document Analysis and Recognition (ICDAR), 2015 13th International Conference on. IEEE, 2015.
- Gomez, Raul, et al. "ICDAR2017 robust reading challenge on COCO-Text." 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2017.
- Ch'ng, Chee Kheng, and Chee Seng Chan. "Total-text: A comprehensive dataset for scene text detection and recognition." Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on. Vol. 1. IEEE, 2017.
- Yuliang, Liu, Lianwen, Jin, et al. "Curved Scene Text Detection via Transverse and Longitudinal Sequence Connection." Pattern Recognition, 2019.
New Challenges for 2019 Announced
Special Issue on Scene Text Reading and its Applications
Do NOT use qq.com emails to register or contact us
Downtime due to scheduled revisions on 26 and 27 March 2018
Downtime due to scheduled revision on 11 and 12 April 2017
1st January to 1st March
i) Q&A period for the competition,
ii) The launching of initial website
15th Feb to 1st March
i) Competition formal announcement,
iii) Sample training images available,
iv) Evaluation protocol, file formats etc. available.
i) Evaluation tools ready,
ii) Full website ready.
i) Competition kicks off officially,
ii) Release of training set images and ground truth.
i) Release of the first part of test set images (2277 images),
ii) Registration closes and website opens for result submission,
iii) Submission opens for 1 page competition report.
i) Release of the result of initial submission.
i) Release of the second part of test set images (2296 images).
i) Deadline of the competition and result submission closes.
i) Submission closes for 1 page competition report.
20th to 25th September
i) Announcement of competition results at ICDAR2019.