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ICDAR 2019 competition on Table Detection and Recognition (cTDaR)
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| Liangcai Gao, Yilun Huang, Hervé Déjean, Jean-Luc Meunier, Qinqin Yan, Yu Fan, Florian Kleber, Eva Lang |
| International Conference on Document Analysis and Recognition (ICDAR), Sydney, Australia, 20-25 September, 2019 |
| Download |
@inproceedings{gao2019icdar,
title={ICDAR 2019 Competition on Table Detection and Recognition (cTDaR)},
author={Gao, Liangcai and Huang, Yilun and D{\'e}jean, Herv{\'e} and Meunier, Jean-Luc and Yan, Qinqin and Fang, Yu and Kleber, Florian and Lang, Eva},
booktitle={2019 International Conference on Document Analysis and Recognition (ICDAR)},
pages={1510--1515},
year={2019},
organization={IEEE}
}The cTDaR competition aims at benchmarking state-of-the-art table detection (TRACK A) and table recognition (TRACK B) methods. In particular, we wish to investigate and compare general methods that can reliably and robustly identify the table regions within a document image on the one hand, and the table structure on the other hand. Due to the presence of hand-drawn tables and handwritten text, the methods must be robust against various noise conditions, interfering annotations, and variations of the tables. Two new challenging datasets were created to test the behaviour of stateof-the-art table detection and recognition systems on real world data. One dataset consists of modern documents, while the other consists of archival documents with presence of handdrawn tables and handwritten text. The evaluation scheme is adapted from the ICDAR 2013 Table competition. After all, we received results of Track A from 11 teams and results of Track B from 2 teams. After comparing these valuable results, our findings are presented in this paper.
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