Overview - ICDAR2019 Robust Reading Challenge on Large-scale Street View Text with Partial Labeling
This challenge focuses on scene text reading in natural images, which can be broken down into scene text detection and spotting problems, based on the proposed Large-scale Street View Text with Partial Labeling (LSVT) dataset. LSVT consists of 450,000 images, is at least 14 times as large as existing robust reading benchmarks [1, 2], and is also the first ever scene text dataset labeled with partial annotations for the text detection and recognition challenges. The amount of fully annotated part of data is also greater than that of previous robust reading benchmarks. There are two main tasks in this competition, which will be detailed in the tasks page.
Competition Report Submission
Participants are expected to send the one page competition report to the ICDARfirstname.lastname@example.org to introduce the submitted methods.
The report should include the descriptions of training data used，developed methods，and testing settings，e.g.，multiscale or model ensembles，etc. Besides, the information of team members，team name and affiliations are necessary to verify the candidates for prize and awards.
The final results will be released after results checking.
1） To confirm the registration in ICDAR-2019 LSVT challenge of the RRC competition 2019, please send an email to ICDARemail@example.com with the title "Participation in the ICDAR-2019 LSVT challenge"
2） This process does not oblige you to participate or submit results, it is an expression of interest. You can participate in one or more tasks of the challenge. It is not obligatory to participate in all the tasks.
LSVT consists of 20,000 testing data, 30,000 training data in full annotations and 400,000 training data in weak annotations, which are referred to as partial labels. We intend to challenge the community to look into novel solutions which can further boost the performance from partial labels. For most of the training data in weak labels, only one transcription per image is provided, which we refer to as `text-of-interest'. All the images were captured from streets, which consist of a large variety of complicated real-world scenarios, e.g., store fronts and landmarks, making the challenge extreme high by narrowing gaps between research and real applications. Examples of these images in full and weak annotations are shown in Fig. 1 and Fig. 2, respectively.
Figure 1. Examples of images with full annotations. The ground truth locations and corresponding text are shown in these images. The labeled characters in yellow include Chinese, numbers and Latin characters. The labeled text regions demonstrate the diversity of text in our dataset, including horizontal text, vertical text, curved text and text with perspective distortion. The horizontal and vertical texts are annotated with quadrilateral, and the curved text instances are annotated with polygon shaped bounding regions.
Figure 2. Examples of images with weak annotations. Note that only the transcription of the text-of-interest in these images
is given as ground truth without location annotations, which is much cheaper to collect.
The regions of "Text-of-interest" usually contain the names of store fonts or descriptions of landmarks, providing meaning information for localization and navigation.
Text instances in the LSVT dataset were annotated with (a) quadrilateral bounding boxes, 8 and 12 vertexes polygon bounding box (more details in ‘tasks’ page), and (b) transcription. Both of these annotations cater for the (a) text detection, (b) text spotting tasks proposed by this challenge.
The prize for ICDAR 2019-LSVT is $8,700 in total, sponsored by Baidu.
Task 1. Text detection, $2,500/$1,250/$600 for top 3 winners.
Task 2. End-to-end text spotting, $2,500/$1,250/$600 for top 3 winners.
Rankings and Results
If you have any questions about the competition, you can contact us by the email: ICDARfirstname.lastname@example.org
 Shi, Baoguang, et al. "ICDAR2017 competition on reading Chinese text in the wild (RCTW-17).", 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). Vol. 1. IEEE, 2017.
 Yuan, Tai Ling, et al. "Chinese Text in the Wild." arXiv preprint arXiv:1803.00085, 2018.
LSVT: The final part of test set available
LSVT: The final part of test set available
LSVT: The first part of test set available
Extended: Special Issue on Scene Text Reading and its Applications
LSVT: Training set available
New Challenges for 2019 Announced
Special Issue on Scene Text Reading and its Applications
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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.
Release of the first part of test set images (10,000 images),
i) Release of the second part of test set images (10,000 images).
ii) Website opens for results submission
i) Deadline of the competition and result submission closes (at PDT 23:59)
ii) Release of the evaluation results.
i) Submission deadline for one page competition report, and the final ranking will be released after results checking.
20th to 25th September
i) Announcement of competition results at ICDAR2019.