ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard Chinese scene text reading is one of the most challenging problems in computer vision and has attracted great interest. Different from English text, Chinese has more than 6000 commonly used characters and Chinese characters can be arranged in various layouts with numerous fonts. The Chinese signboards in street view are a good choice for Chinese scene text images since they have different backgrounds, fonts and layouts. We organized a competition called ICDAR2019ReCTS, which mainly focuses on reading Chinese text on signboard. This report presents the final results of the competition. A largescale dataset of 25,000 annotated signboard images, in which all the text lines and characters are annotated with locations and transcriptions, were released. Four tasks, namely character recognition, text line recognition, text line detection and endtoend recognition were set up. Besides, considering the Chinese text ambiguity issue, we proposed a multi ground truth (multiGT) evaluation method to make evaluation fairer. The competition started on March 1, 2019 and ended on April 30, 2019. 262 submissions from 46 teams are received. Most of the participants come from universities, research institutes, and tech companies in China. There are also some participants from the United States, Australia, Singapore, and Korea. 21 teams submit results for Task 1, 23 teams submit results for Task 2, 24 teams submit results for Task 3, and 13 teams submit results for Task 4. The official website for the competition is http:rrc.cvc.uab.es?ch12.

ICDAR2019 Robust Reading Challenge on ArbitraryShaped Text  RRCArT This paper reports the ICDAR2019 Robust Reading Challenge on ArbitraryShaped Text  RRCArT that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting. A total of 78 submissions from 46 unique teamsindividuals were received for this competition. The top performing score of each challenge is as follows: i) T1  82.65%, ii) T2.1  74.3%, iii) T2.2  85.32%, iv) T3.1  53.86%, and v) T3.2  54.91%. Apart from the results, this paper also details the ArT dataset, tasks description, evaluation metrics and participantsu0027 methods. The dataset, the evaluation kit as well as the results are publicly available at the challenge website.