Table understanding in structured documents

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10473073" target="_blank" >RIV/00216208:11320/19:10473073 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/ICDARW.2019.40098" target="_blank" >https://doi.org/10.1109/ICDARW.2019.40098</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICDARW.2019.40098" target="_blank" >10.1109/ICDARW.2019.40098</a>

Result language

angličtina

Original language name

Table understanding in structured documents

Original language description

Table detection and extraction has been studied in the context of documents like reports, where tables are clearly outlined and stand out from the document structure visually. We study this topic in a rather more challenging domain of layout-heavy business documents, particularly invoices. Invoices present the novel challenges of tables being often without outlines - either in the form of borders or surrounding text flow - with ragged columns and widely varying data content. We will also show, that we can extract specific information from structurally different tables or table-like structures with one model. We present a comprehensive representation of a page using graph over word boxes, positional embeddings, trainable textual features and rephrase the table detection as a text box labeling problem. We will work on our newly presented dataset of pro forma invoices, invoices and debit note documents using this representation and propose multiple baselines to solve this labeling problem. We then propose a novel neural network model that achieves strong, practical results on the presented dataset and analyze the model performance and effects of graph convolutions and self-attention in detail.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2019 INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION WORKSHOPS (ICDARW), VOL 5

ISBN

978-1-72815-054-3

ISSN

1520-5363

e-ISSN

—

Number of pages

7

Pages from-to

158-164

Publisher name

IEEE

Place of publication

NEW YORK

Event location

Sydney

Event date

Sep 19, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000518786800027