dc.contributor.author |
Stauffer, Michael
|
|
dc.contributor.author |
Fischer, Andreas
|
|
dc.contributor.author |
Riesen, Kaspar
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|
dc.date.accessioned |
2018-04-25T09:23:10Z |
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dc.date.issued |
2018-09 |
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dc.description.abstract |
In the last decades historical handwritten documents have become increasingly available in digital form. Yet, the accessibility to these documents with respect to browsing and searching remained limited as full automatic transcription is often not possible or not sufficiently accurate. This paper proposes a novel reliable approach for template-based keyword spotting in historical handwritten documents. In particular, our framework makes use of different graph representations for segmented word images and a sophisticated matching procedure. Moreover, we extend our method to a spotting ensemble. In an exhaustive experimental evaluation on four widely used benchmark datasets we show that the proposed approach is able to keep up or even outperform several state-of-the-art methods for template- and learning-based keyword spotting. |
en_ZA |
dc.description.department |
Informatics |
en_ZA |
dc.description.embargo |
2019-09-01 |
|
dc.description.librarian |
hj2018 |
en_ZA |
dc.description.sponsorship |
The Hasler Foundation Switzerland |
en_ZA |
dc.description.uri |
http://www.elsevier.com/locate/patcog |
en_ZA |
dc.identifier.citation |
Stauffer, M., Fischer, A. & Riesen, K. 2018, 'Keyword spotting in historical handwritten documents based on graph matching', Pattern Recognition, vol. 81, pp. 240-253. |
en_ZA |
dc.identifier.issn |
0031-3203 |
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dc.identifier.other |
10.1016/j.patcog.2018.04.001 |
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dc.identifier.uri |
http://hdl.handle.net/2263/64716 |
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dc.language.iso |
en |
en_ZA |
dc.publisher |
Elsevier |
en_ZA |
dc.rights |
© 2018 Elsevier Ltd. All rights reserved.Notice : this is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in Pattern Recognition, vol. 81, pp. 240-253, 2018. doi : 10.1016/j.patcog.2018.04.001. |
en_ZA |
dc.subject |
Handwritten keyword spotting |
en_ZA |
dc.subject |
Ensemble methods |
en_ZA |
dc.subject |
Bipartite graph matching |
en_ZA |
dc.subject |
Graph representation |
en_ZA |
dc.subject |
Pattern recognition |
en_ZA |
dc.subject |
State-of-the-art methods |
en_ZA |
dc.subject |
Software engineering |
en_ZA |
dc.subject |
Experimental evaluation |
en_ZA |
dc.subject |
Automatic transcription |
en_ZA |
dc.title |
Keyword spotting in historical handwritten documents based on graph matching |
en_ZA |
dc.type |
Postprint Article |
en_ZA |