How to Segment Handwritten Historical Chronicles Using Fully Convolutional Networks?
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F22%3A43965692" target="_blank" >RIV/49777513:23520/22:43965692 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-10161-8_9" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-10161-8_9</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-10161-8_9" target="_blank" >10.1007/978-3-031-10161-8_9</a>
Alternative languages
Result language
angličtina
Original language name
How to Segment Handwritten Historical Chronicles Using Fully Convolutional Networks?
Original language description
This paper deals with historical document image segmentation with focus on chronicles available in the Porta fontium portal. We build on our previously published database that has precise pixel-level annotations in PAGE format but also utilise other datasets for transfer learning in order to improve the results. We discuss a series of experiments that evaluate possibilities how to train a neural model for image, text and background segmentation. The outcome, in a form of segmentation method with relatively low computational costs and great results, is integrated into the Porta fontium portal to improve its possibilities of searching and publication of the documents.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Agents and Artificial Intelligence : Lecture Notes in Computer Science
ISBN
978-3-031-10160-1
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
16
Pages from-to
181-196
Publisher name
Springer
Place of publication
Cham
Event location
Virtual Event
Event date
Feb 4, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000876376200009