@InProceedings{YipengSun2019,
author="Yipeng Sun
and Zihan Ni
and Chee-Kheng Chng
and Yuliang Liu
and Canjie Luo
and Chun Chet Ng
and Junyu Han
and Errui Ding
and Jingtuo Liu
and Dimosthenis Karatzas
and Chee Seng Chan
and Lianwen Jin",
title="ICDAR 2019 Competition on Large-Scale Street View Text with Partial Labeling -- RRC-LSVT",
booktitle="15th International Conference on Document Analysis and Recognition",
year="2019",
pages="1557--1562",
abstract="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.",
optnote="DAG; 600.129; 600.121",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3339), last updated on Tue, 25 Jan 2022 10:50:42 +0100",
doi="10.1109/ICDAR.2019.00250",
opturl="https://ieeexplore.ieee.org/document/8978143",
file=":http://refbase.cvc.uab.es/files/SNC2019.pdf:PDF"
}