Filters for graph-based keyword spotting in historical handwritten documents

Stauffer, Michael (University of Applied Sciences and Arts Northwestern Switzerland, Institute for Information Systems, Olten, Switzerland ; University of Pretoria, Department of Informatics, Pretoria, South Africa) ; Fischer, Andreas (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland ; Department of Informatics, University of Fribourg, Fribourg, Switzerland) ; Riesen, Kaspar (University of Applied Sciences and Arts Northwestern Switzerland, Institute for Information Systems, Olten, Switzerland)

The accessibility to handwritten historical documents is often constrained by the limited feasibility of automatic full transcriptions.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 In an experimental evaluation on four different historical documents, we show that about 90% of the matchings can be omitted, while the KWS accuracy is not negatively affected.


Keywords:
Article Type:
scientifique
Faculty:
Ingénierie et Architecture
School:
HEIA-FR
Institute:
iCoSys - Institut des systèmes complexes
Subject(s):
Ingénierie
Date:
2018-03
Published in:
Pattern Recognition Letters
DOI:
ISSN:
0167-8655
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2019-02-26, last modified 2019-03-05

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