The final publication is available via <a href="https://doi.org/10.1109/ICDAR.2017.225" target="_blank">https://doi.org/10.1109/ICDAR.2017.225</a>.
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dc.description.abstract
The ICDAR 2017 Competition on Historical Document Writer Identification is dedicated to record the most recent advances made in the field of writer identification. The goal of the writer identification task is the retrieval of pages, which have been written by the same author. The test dataset used in this competition consists of 3600 handwritten pages originating from 13th to 20th century. It contains manuscripts from 720 different writers where each writer contributed five pages. This paper describes the dataset, as well as the details of the competition. Five different institutions submitted six methods which were ranked using identification and retrieval metrics. The paper describes the competition details including the dataset, the evaluation measures used as well as a short description of each submitted method.
en
dc.description.sponsorship
European Union's Horizon 2020
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dc.language
English
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dc.language.iso
en
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dc.publisher
Kyoto Japan
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
Schreiberidentifizierung
de
dc.subject
Writer Identification
en
dc.subject
Competition
en
dc.subject
Dataset
en
dc.subject
Compter vision
en
dc.title
ICDAR2017 Competition on Historical Document Writer Identification (Historical-WI)
National Centre of Scientific Research "Demokritos", Greece
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dc.contributor.affiliation
National Centre of Scientific Research "Demokritos", Greece
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dc.contributor.affiliation
National Centre of Scientific Research "Demokritos", Greece
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dc.relation.isbn
9781538635865
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dc.relation.doi
10.1109/ICDAR43114.2017
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dc.relation.grantno
674943
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dc.rights.holder
2017, IEEE Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
2017 14th IAPR International Conference on Document Analysis and Recognition : ICDAR 2017
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tuw.version
am
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tuw.publication.orgunit
E193 - Institut für Rechnergestützte Automation
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tuw.publisher.doi
10.1109/ICDAR.2017.225
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dc.identifier.libraryid
AC11365320
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dc.description.numberOfPages
6
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dc.identifier.urn
urn:nbn:at:at-ubtuw:3-3375
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tuw.author.orcid
0000-0001-5033-6723
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tuw.author.orcid
0000-0001-8351-5066
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tuw.author.orcid
0000-0002-5048-5128
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tuw.author.orcid
0000-0003-0455-3799
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dc.rights.identifier
Urheberrechtsschutz
de
dc.rights.identifier
In Copyright
en
tuw.event.name
2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)
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tuw.event.startdate
09-11-2017
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tuw.event.enddate
15-11-2017
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tuw.event.online
On Site
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tuw.event.type
Event for scientific audience
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tuw.event.place
Kyoto
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tuw.event.country
JP
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tuw.event.presenter
Fiel, Stefan
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item.openairecristype
http://purl.org/coar/resource_type/c_5794
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item.openaccessfulltext
Open Access
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item.openairetype
conference paper
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item.fulltext
with Fulltext
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item.languageiso639-1
en
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item.grantfulltext
open
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item.cerifentitytype
Publications
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crisitem.author.dept
E193-01 - Forschungsbereich Computer Vision
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crisitem.author.dept
E193-01 - Forschungsbereich Computer Vision
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crisitem.author.dept
E193-01 - Forschungsbereich Computer Vision
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crisitem.author.dept
Friedrich-Alexander-Universität Erlangen-Nürnberg
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crisitem.author.dept
National Centre of Scientific Research "Demokritos"
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crisitem.author.dept
National Centre of Scientific Research "Demokritos"
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crisitem.author.dept
National Centre of Scientific Research "Demokritos"
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crisitem.author.orcid
0000-0001-8351-5066
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crisitem.author.orcid
0000-0002-5048-5128
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crisitem.author.parentorg
E193 - Institut für Visual Computing and Human-Centered Technology
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crisitem.author.parentorg
E193 - Institut für Visual Computing and Human-Centered Technology
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crisitem.author.parentorg
E193 - Institut für Visual Computing and Human-Centered Technology
