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Speeding-Up Graph-Based Keyword Spotting by Quadtree Segmentations

  • Michael Stauffer
  • Andreas Fischer
  • Kaspar Riesen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10424)

Abstract

Keyword Spotting (KWS) improves the accessibility to handwritten historical documents by unconstrained retrievals of keywords. The proposed KWS framework operates on segmented words that are in turn represented as graphs. The actual KWS process is based on matching graphs by means of a cubic-time graph matching algorithm. Although this matching algorithm is quite efficient, the polynomial time complexity might still be a limiting factor (especially in case of large documents). The present paper introduces a novel approach that aims at speeding up the retrieval process. The basic idea is to first segment individual graphs into smaller subgraphs by means of a quadtree procedure. Eventually, the graph matching procedure can be conducted on the resulting pairs of smaller subgraphs. In an experimental evaluation on two benchmark datasets we empirically confirm substantial speed-ups while the KWS accuracy is nearly not affected.

Keywords

Handwritten keyword spotting Bipartite graph matching Quadtree graph segmentation 

Notes

Acknowledgments

This work has been supported by the Hasler Foundation Switzerland.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Michael Stauffer
    • 1
    • 4
  • Andreas Fischer
    • 2
    • 3
  • Kaspar Riesen
    • 1
  1. 1.Institute for Information SystemsUniversity of Applied Sciences and Arts Northwestern SwitzerlandOltenSwitzerland
  2. 2.Department of InformaticsUniversity of FribourgFribourgSwitzerland
  3. 3.Institute for Complex SystemsUniversity of Applied Sciences and Arts Western SwitzerlandFribourgSwitzerland
  4. 4.Department of InformaticsUniversity of PretoriaPretoriaSouth Africa

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