Keyword spotting in historical handwritten documents based on graph matching
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Author (Corporation)
Publication date
2018
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Collections
Type
01A - Journal article
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Supervisor
Parent work
Pattern Recognition
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DOI of the original publication
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Series
Series number
Volume
81
Issue / Number
Pages / Duration
240-253
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Publisher / Publishing institution
Elsevier
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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.
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Subject (DDC)
330 - Wirtschaft
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ISBN
ISSN
0031-3203
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
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Peer review of the complete publication
Open access category
Closed
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Citation
STAUFFER, Michael, Andreas FISCHER und Kaspar RIESEN, 2018. Keyword spotting in historical handwritten documents based on graph matching. Pattern Recognition. 2018. Bd. 81, S. 240–253. DOI 10.1016/j.patcog.2018.04.001. Verfügbar unter: https://irf.fhnw.ch/handle/11654/42316