<div class="csl-bib-body">
<div class="csl-entry">Keglevic, M., Fiel, S., & Sablatnig, R. (2018). Learning Features for Writer Retrieval and Identification using Triplet CNNs. In <i>16th International Conference on Frontiers in Handwriting Recognition (ICFHR 2018)</i>. Niagara Falls, New York, USA. https://doi.org/10.1109/ICFHR-2018.2018.00045</div>
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The final publication is available via <a href="https://doi.org/10.1109/ICFHR-2018.2018.00045" target="_blank">https://doi.org/10.1109/ICFHR-2018.2018.00045</a>.
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dc.description.abstract
This paper presents a method for writer retrieval and identification using a feature descriptor learned by a Convolutional Neural Network. Instead of using a network for classification, we propose the use of a triplet network that learns a similarity measure for image patches. Patches of the handwriting are extracted and mapped into an embedding where this similarity measure is defined by the L2 distance. The triplet network is trained by maximizing the interclass distance, while minimizing the intraclass distance in this embedding. The image patches are encoded using the learned feature descriptor. By applying the Vector of Locally Aggregated Descriptors encoding to these features, we generate a feature vector for each document image. A detailed parameter evaluation is given which shows that this method achieves a mean average precision of 86.1% on the ICDAR 2013 writer identification dataset, but future work has to be done to improve the performance on historic datasets. In addition, the strategy for clustering the feature space is investigated.
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.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
Writer Identification
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dc.subject
Writer Retrieval
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dc.subject
Document Analysis
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dc.title
Learning Features for Writer Retrieval and Identification using Triplet CNNs
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dc.type
Inproceedings
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dc.type
Konferenzbeitrag
de
dc.rights.license
Urheberrechtsschutz
de
dc.rights.license
In Copyright
en
dc.relation.publication
16th International Conference on Frontiers in Handwriting Recognition (ICFHR 2018)
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dc.relation.isbn
9781538658758
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dc.relation.grantno
674943
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dc.rights.holder
2018 IEEE
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dc.type.category
Full-Paper Contribution
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tuw.relation.publisher
Niagara Falls, New York, USA
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tuw.version
am
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tuw.publication.orgunit
E193 - Institut für Visual Computing and Human-Centered Technology
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tuw.publisher.doi
10.1109/ICFHR-2018.2018.00045
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dc.identifier.libraryid
AC15147545
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dc.identifier.urn
urn:nbn:at:at-ubtuw:3-3767
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tuw.author.orcid
0000-0002-4644-2723
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tuw.author.orcid
0000-0001-5033-6723
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tuw.author.orcid
0000-0003-4195-1593
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dc.rights.identifier
Urheberrechtsschutz
de
dc.rights.identifier
In Copyright
en
item.fulltext
with Fulltext
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item.cerifentitytype
Publications
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item.openairecristype
http://purl.org/coar/resource_type/c_5794
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item.languageiso639-1
en
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item.openaccessfulltext
Open Access
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item.openairetype
conference paper
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item.grantfulltext
open
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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 - Institut für Visual Computing and Human-Centered Technology
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crisitem.author.orcid
0000-0002-4644-2723
-
crisitem.author.orcid
0000-0003-4195-1593
-
crisitem.author.parentorg
E193 - Institut für Visual Computing and Human-Centered Technology
-
crisitem.author.parentorg
E193 - Institut für Visual Computing and Human-Centered Technology