Graph-based keyword spotting in historical documents using context-aware Hausdorff edit distance
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2018
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04B - Conference paper
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13th IAPR International Workshop on Document Analysis Systems. DAS 2018. Proceedings
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49-54
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IEEE
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New York
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Abstract
Scanned handwritten historical documents are often not well accessible due to the limited feasibility of automatic full transcriptions. Thus, Keyword Spotting (KWS) has been proposed as an alternative to retrieve arbitrary query words from this kind of documents. In the present paper, word images are represented by means of graphs. That is, a graph is used to represent the inherent topological characteristics of handwriting. The actual keyword spotting is then based on matching a query graph with all document graphs. In particular, we make use of a fast graph matching algorithm that considers the contextual substructure of nodes. The motivation for this inclusion of node context is to increase the overall KWS accuracy. In an experimental evaluation on four historical documents, we show that the proposed procedure clearly outperforms diverse other template-based reference systems. Moreover, our novel framework keeps up or even outperforms many state-of-the-art learning-based KWS approaches.
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13th IAPR International Workshop on Document Analysis Systems (DAS 2018)
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Conference start date
24.04.2018
Conference end date
27.04.2018
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English
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Yes
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Published
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Citation
Stauffer, M., Fischer, A., & Riesen, K. (2018). Graph-based keyword spotting in historical documents using context-aware Hausdorff edit distance. 13th IAPR International Workshop on Document Analysis Systems. DAS 2018. Proceedings, 49–54. https://doi.org/10.1109/DAS.2018.31