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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.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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