ICDAR 2019 Competition on LargeScale Street View Text with Partial Labeling  RRCLSVT Robust text reading from street view images provides valuable information for various applications. Performance improvement of existing methods in such a challenging scenario heavily relies on the amount of fully annotated training data, which is costly and inefficient to obtain. To scale up the amount of training data while keeping the labeling procedure costeffective, this competition introduces a new challenge on Largescale Street View Text with Partial Labeling (LSVT), providing 5,0000 and 400,000 images in full and weak annotations, respectively. This competition aims to explore the abilities of stateoftheart methods to detect and recognize text instances from largescale street view images, closing gaps between research benchmarks and real applications. During the competition period, a total number of 41 teams participate in the two tasks with 132 valid submissions, i.e., text detection and endtoend text spotting. This paper includes dataset descriptions, task definitions, evaluation protocols and results summaries of ICDAR 2019LSVT 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.