<div class="csl-bib-body">
<div class="csl-entry">Fiel, S. (2015). <i>Novel methods for writer identification and retrieval</i> [Dissertation, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2015.25468</div>
</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2015.25468
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/3591
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dc.description
Zusammenfassung in deutscher Sprache
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dc.description.abstract
Writer identification is the task of identifying the writer of a handwritten document, based on a set of documents where the authors are known. It can be used e.g. for tasks in forensics and for historical document analysis. In contrast to this, writer retrieval is to receive a ranking of the pages in the set of documents sorted according to the similarity of handwriting and can be used for clustering a not indexed set of documents according to the individual handwriting. State-of-the-art methods calculate features on the contours of the characters, so pre-processing steps are needed to extract this contour. In contrast to this in this thesis, three novel approaches for writer identification and writer retrieval are presented. The first is based on the bag of words approach, which is well known for object recognition. SIFT features are calculated on the handwriting and then an occurrence histogram is generated which is then used for the identification of the writer. The second method is based on the Fisher vector. Again, SIFT features are generated on the handwriting, but this time the gradient vectors of a Gaussian Mixture Model (GMM) are used to generate the feature vector for writer identification. The last method is based on Convolutional Neural Network (CNN). A CNN is trained on image patches and the classification layer is cut off and the second last layer is used as feature vector for this patch. The mean vector of all patches on one page is the feature vector for the handwriting and is used for identification and retrieval. The methods presented are evaluated and compared to the state of the art on different scientific databases and additionally on a historic dataset using common evaluation metrics for writer identification. The evaluations show that the three methods proposed outperform the state of the art on many of the different tasks on these datasets. Advantages and possible weaknesses are discussed. The methods proposed achieve good results (>90%) on every dataset used for evaluation.
en
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
Fisher Vector
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dc.subject
Deep Learning
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dc.subject
Document Analysis
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dc.title
Novel methods for writer identification and retrieval
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dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2015.25468
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Stefan Fiel
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dc.publisher.place
Wien
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tuw.version
vor
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tuw.thesisinformation
Technische Universität Wien
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tuw.publication.orgunit
E183 - Institut für Rechnergestützte Automation
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dc.type.qualificationlevel
Doctoral
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dc.identifier.libraryid
AC13006439
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dc.description.numberOfPages
118
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dc.identifier.urn
urn:nbn:at:at-ubtuw:1-89519
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dc.thesistype
Dissertation
de
dc.thesistype
Dissertation
en
tuw.author.orcid
0000-0001-5033-6723
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dc.rights.identifier
In Copyright
en
dc.rights.identifier
Urheberrechtsschutz
de
tuw.advisor.staffStatus
staff
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tuw.advisor.orcid
0000-0003-4195-1593
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item.fulltext
with Fulltext
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item.cerifentitytype
Publications
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item.mimetype
application/pdf
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item.openairecristype
http://purl.org/coar/resource_type/c_db06
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item.languageiso639-1
en
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item.openaccessfulltext
Open Access
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item.openairetype
doctoral thesis
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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.parentorg
E193 - Institut für Visual Computing and Human-Centered Technology