dc.contributor.author |
Stauffer, Michael
|
|
dc.contributor.author |
Fischer, Andreas
|
|
dc.contributor.author |
Riesen, Kaspar
|
|
dc.date.accessioned |
2018-04-25T09:39:29Z |
|
dc.date.issued |
2020-06 |
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dc.description.abstract |
The accessibility to handwritten historical documents is often constrained by the limited feasibility of automatic full transcriptions. Keyword Spotting (KWS), that allows to retrieve arbitrary query words from documents, has been proposed as alternative. In the present paper, we make use of graphs for representing word images. The actual keyword spotting is thus based on matching a query graph with all documents graphs. However, even with relative fast approximation algorithms the shear amount of matchings might limit the practical application of this approach. For this reason we present two novel filters with linear time complexity that allow to substantially reduce the number of graph matchings actually required. In particular, these filters estimate a graph dissimilarity between a query graph and all document graphs based on their node and edge distribution in a polar coordinate system. Eventually, all graphs from the document with distributions that differ to heavily from the query’s node/edge distribution are eliminated. In an experimental evaluation on four different historical documents, we show that about 90% of the matchings can be omitted, while the KWS accuracy is not negatively affected. |
en_ZA |
dc.description.department |
Informatics |
en_ZA |
dc.description.embargo |
2019-03-30 |
|
dc.description.librarian |
hj2018 |
en_ZA |
dc.description.sponsorship |
The Hasler Foundation Switzerland. |
en_ZA |
dc.description.uri |
http://www.elsevier.com/locate/patrec |
en_ZA |
dc.identifier.citation |
Stauffer, M., Fischer, A. & Riesen, K. 2020, 'Filters for graph-based keyword spotting in historical handwritten documents', Pattern Recognition Letters, vol. 134, pp. 125-134. |
en_ZA |
dc.identifier.issn |
0167-8655 (print) |
|
dc.identifier.issn |
1872-7344 (online) |
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dc.identifier.other |
10.1016/j.patrec.2018.03.030 |
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dc.identifier.uri |
http://hdl.handle.net/2263/64717 |
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dc.language.iso |
en |
en_ZA |
dc.publisher |
Elsevier |
en_ZA |
dc.rights |
© 2018 Elsevier B.V. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. 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 Letters, vol. 134, pp. 125-134, 2020. doi : 10.1016/j.patrec.2018.03.030. |
en_ZA |
dc.subject |
Graph theory |
en_ZA |
dc.subject |
Keyword spotting |
en_ZA |
dc.subject |
Graph representation |
en_ZA |
dc.subject |
Filter method |
en_ZA |
dc.subject |
Fast rejection |
en_ZA |
dc.subject |
Pattern matching |
en_ZA |
dc.subject |
History |
en_ZA |
dc.subject |
Graphic methods |
en_ZA |
dc.subject |
Bandpass filters |
en_ZA |
dc.subject |
Approximation algorithms |
en_ZA |
dc.subject |
Handwritten keyword spotting |
en_ZA |
dc.subject |
Bipartite graph matching |
en_ZA |
dc.title |
Filters for graph-based keyword spotting in historical handwritten documents |
en_ZA |
dc.type |
Postprint Article |
en_ZA |