Page Layout Analysis System for Unconstrained Historic Documents

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F21%3APU142905" target="_blank" >RIV/00216305:26230/21:PU142905 - isvavai.cz</a>

Result on the web

<a href="https://www.fit.vut.cz/research/publication/12493/" target="_blank" >https://www.fit.vut.cz/research/publication/12493/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-86331-9_32" target="_blank" >10.1007/978-3-030-86331-9_32</a>

Result language

angličtina

Original language name

Page Layout Analysis System for Unconstrained Historic Documents

Original language description

Extraction of text regions and individual text lines from historic documents is necessary for automatic transcription. We propose extending a CNN-based text baseline detection system by adding line height and text block boundary predictions to the model output, allowing the system to extract more comprehensive layout information. We also show that pixel-wise text orientation prediction can be used for processing documents with multiple text orientations. We demonstrate that the proposed method performs well on the cBAD baseline detection dataset. Additionally, we benchmark the method on newly introduced PERO layout dataset which we also make public.

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

<a href="/en/project/DG18P02OVV055" target="_blank" >DG18P02OVV055: Advanced content extraction and recognition for printed and handwritten documents for better accessibility and usability</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

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

Article name in the collection

Lladós J., Lopresti D., Uchida S. (eds) Document Analysis and Recognition - ICDAR 2021

ISBN

978-3-030-86330-2

ISSN

—

e-ISSN

—

Number of pages

15

Pages from-to

492-506

Publisher name

Springer Nature Switzerland AG

Place of publication

Lausanne

Event location

Lausanne, Switzerland

Event date

Sep 5, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000770800600032