@inproceedings{katti-etal-2018-chargrid,
title = "{C}hargrid: Towards Understanding 2{D} Documents",
author = {Katti, Anoop R and
Reisswig, Christian and
Guder, Cordula and
Brarda, Sebastian and
Bickel, Steffen and
H{\"o}hne, Johannes and
Faddoul, Jean Baptiste},
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1476",
doi = "10.18653/v1/D18-1476",
pages = "4459--4469",
abstract = "We introduce a novel type of text representation that preserves the 2D layout of a document. This is achieved by encoding each document page as a two-dimensional grid of characters. Based on this representation, we present a generic document understanding pipeline for structured documents. This pipeline makes use of a fully convolutional encoder-decoder network that predicts a segmentation mask and bounding boxes. We demonstrate its capabilities on an information extraction task from invoices and show that it significantly outperforms approaches based on sequential text or document images.",
}
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%0 Conference Proceedings
%T Chargrid: Towards Understanding 2D Documents
%A Katti, Anoop R.
%A Reisswig, Christian
%A Guder, Cordula
%A Brarda, Sebastian
%A Bickel, Steffen
%A Höhne, Johannes
%A Faddoul, Jean Baptiste
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F katti-etal-2018-chargrid
%X We introduce a novel type of text representation that preserves the 2D layout of a document. This is achieved by encoding each document page as a two-dimensional grid of characters. Based on this representation, we present a generic document understanding pipeline for structured documents. This pipeline makes use of a fully convolutional encoder-decoder network that predicts a segmentation mask and bounding boxes. We demonstrate its capabilities on an information extraction task from invoices and show that it significantly outperforms approaches based on sequential text or document images.
%R 10.18653/v1/D18-1476
%U https://aclanthology.org/D18-1476
%U https://doi.org/10.18653/v1/D18-1476
%P 4459-4469
Markdown (Informal)
[Chargrid: Towards Understanding 2D Documents](https://aclanthology.org/D18-1476) (Katti et al., EMNLP 2018)
ACL
- Anoop R Katti, Christian Reisswig, Cordula Guder, Sebastian Brarda, Steffen Bickel, Johannes Höhne, and Jean Baptiste Faddoul. 2018. Chargrid: Towards Understanding 2D Documents. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4459–4469, Brussels, Belgium. Association for Computational Linguistics.