PT Unknown
AU Yipeng Sun
   Zihan Ni
   Chee-Kheng Chng
   Yuliang Liu
   Canjie Luo
   Chun Chet Ng
   Junyu Han
   Errui Ding
   Jingtuo Liu
   Dimosthenis Karatzas
   Chee Seng Chan
   Lianwen Jin
TI ICDAR 2019 Competition on Large-Scale Street View Text with Partial Labeling – RRC-LSVT
BT 15th International Conference on Document Analysis and Recognition
PY 2019
BP 1557
EP 1562
DI 10.1109/ICDAR.2019.00250
AB 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 in-efficient to obtain. To scale up the amount of training data while keeping the labeling procedure cost-effective, this competition introduces a new challenge on Large-scale Street View Text with Partial Labeling (LSVT), providing 50, 000 and 400, 000 images in full and weak annotations, respectively. This competition aims to explore the abilities of state-of-the-art methods to detect and recognize text instances from large-scale street view images, closing the gap between research benchmarks and real applications. During the competition period, a total of 41 teams participated in the two proposed tasks with 132 valid submissions, ie, text detection and end-to-end text spotting. This paper includes dataset descriptions, task definitions, evaluation protocols and results summaries of the ICDAR 2019-LSVT challenge.
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