<div class="csl-bib-body">
<div class="csl-entry">Peer, M., Kleber, F., & Sablatnig, R. (2023). Towards Writer Retrieval for Historical Datasets. In <i>Document Analysis and Recognition - ICDAR 2023</i> (pp. 411–427). https://doi.org/10.1007/978-3-031-41676-7_24</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/190315
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dc.description.abstract
This paper presents an unsupervised approach for writer retrieval based on clustering SIFT descriptors detected at keypoint locations resulting in pseudo-cluster labels. With those cluster labels, a residual network followed by our proposed NetRVLAD, an encoding layer with reduced complexity compared to NetVLAD, is trained on 32 × 32 patches at keypoint locations. Additionally, we suggest a graph-based reranking algorithm called SGR to exploit similarities of the page embeddings to boost the retrieval performance. Our approach is evaluated on two historical datasets (Historical-WI and HisIR19). We include an evaluation of different backbones and NetRVLAD. It competes with related work on historical datasets without using explicit encodings. We set a new State-of-the-art on both datasets by applying our reranking scheme and show that our approach achieves comparable performance on a modern dataset as well.
en
dc.language.iso
en
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dc.relation.ispartofseries
Lecture Notes in Computer Science
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dc.subject
Document Analysis
en
dc.subject
NetVLAD
en
dc.subject
Reranking
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dc.subject
Writer Retrieval
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dc.title
Towards Writer Retrieval for Historical Datasets
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.relation.isbn
978-3-031-41676-7
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dc.description.startpage
411
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dc.description.endpage
427
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
Document Analysis and Recognition - ICDAR 2023
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tuw.container.volume
14187
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tuw.peerreviewed
true
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tuw.researchTopic.id
I5
-
tuw.researchTopic.name
Visual Computing and Human-Centered Technology
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tuw.researchTopic.value
100
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tuw.publication.orgunit
E193-01 - Forschungsbereich Computer Vision
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tuw.publisher.doi
10.1007/978-3-031-41676-7_24
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dc.description.numberOfPages
17
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tuw.author.orcid
0000-0001-6843-0830
-
tuw.author.orcid
0000-0001-8351-5066
-
tuw.author.orcid
0000-0003-4195-1593
-
tuw.event.name
The 17th International Conference on Document Analysis and Recognition - ICDAR2023
en
tuw.event.startdate
21-08-2023
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tuw.event.enddate
26-08-2023
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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
San José, Kalifornien
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tuw.event.country
US
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tuw.event.presenter
Peer, Marco
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tuw.event.track
Multi Track
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wb.sciencebranch
Informatik
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wb.sciencebranch
Mathematik
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wb.sciencebranch.oefos
1020
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wb.sciencebranch.oefos
1010
-
wb.sciencebranch.value
90
-
wb.sciencebranch.value
10
-
item.languageiso639-1
en
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item.openairetype
conference paper
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item.grantfulltext
none
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item.fulltext
no 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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crisitem.author.dept
E193-01 - Forschungsbereich Computer Vision
-
crisitem.author.dept
E193-01 - Forschungsbereich Computer Vision
-
crisitem.author.dept
E193 - Institut für Visual Computing and Human-Centered Technology
-
crisitem.author.orcid
0000-0001-6843-0830
-
crisitem.author.orcid
0000-0001-8351-5066
-
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