%0 Conference Proceedings %T ICDAR 2019 Competition on Large-Scale Street View Text with Partial Labeling – RRC-LSVT %A Yipeng Sun %A Zihan Ni %A Chee-Kheng Chng %A Yuliang Liu %A Canjie Luo %A Chun Chet Ng %A Junyu Han %A Errui Ding %A Jingtuo Liu %A Dimosthenis Karatzas %A Chee Seng Chan %A Lianwen Jin %B 15th International Conference on Document Analysis and Recognition %D 2019 %F Yipeng Sun2019 %O DAG; 600.129; 600.121 %O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3339), last updated on Tue, 25 Jan 2022 10:50:42 +0100 %X 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. %U https://ieeexplore.ieee.org/document/8978143 %U http://refbase.cvc.uab.es/files/SNC2019.pdf %U http://dx.doi.org/10.1109/ICDAR.2019.00250 %P 1557-1562