ICDAR2019 Robust Reading Challenge on Multilingual Scene Text Detection and Recognition xe2x80x94 RRCMLT2019 With the growing cosmopolitan culture of modern cities, the need of robust MultiLingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and push the stateoftheart forward, the proposed competition builds on top of the RRCMLT2017 with an additional endtoend task, an additional language in the real images dataset, a large scale multilingual synthetic dataset to assist the training, and a baseline EndtoEnd recognition method. The real dataset consists of 20,000 images containing text from 10 languages. The challenge has 4 tasks covering various aspects of multilingual scene text: (a) text detection, (b) cropped word script classification, (c) joint text detection and script classification and (d) endtoend detection and recognition. In total, the competition received 60 submissions from the research and industrial communities. This paper presents the dataset, the tasks and the findings of the presented RRCMLT2019 challenge.

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.