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A Fast Matching Algorithm for Graph-Based Handwriting Recognition

  • Andreas Fischer
  • Ching Y. Suen
  • Volkmar Frinken
  • Kaspar Riesen
  • Horst Bunke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7877)

Abstract

The recognition of unconstrained handwriting images is usually based on vectorial representation and statistical classification. Despite their high representational power, graphs are rarely used in this field due to a lack of efficient graph-based recognition methods. Recently, graph similarity features have been proposed to bridge the gap between structural representation and statistical classification by means of vector space embedding. This approach has shown a high performance in terms of accuracy but had shortcomings in terms of computational speed. The time complexity of the Hungarian algorithm that is used to approximate the edit distance between two handwriting graphs is demanding for a real-world scenario. In this paper, we propose a faster graph matching algorithm which is derived from the Hausdorff distance. On the historical Parzival database it is demonstrated that the proposed method achieves a speedup factor of 12.9 without significant loss in recognition accuracy.

Keywords

Edit Distance Edit Operation Handwriting Recognition Graph Size Hungarian Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Andreas Fischer
    • 1
  • Ching Y. Suen
    • 1
  • Volkmar Frinken
    • 2
  • Kaspar Riesen
    • 3
  • Horst Bunke
    • 4
  1. 1.Centre for Pattern Recognition and Machine IntelligenceConcordia UniversityMontrealCanada
  2. 2.Computer Vision Center, Dept. of Computer ScienceUniversitat Autònoma de BarcelonaBellaterraSpain
  3. 3.Institute for Informations SystemsUniversity of Applied Sciences and Arts Northwestern SwitzerlandOltenSwitzerland
  4. 4.Institute of Computer Science and Applied MathematicsUniversity of BernBernSwitzerland

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