Using Strings for On-Line Handwriting Shape Matching: A New Weighted Edit Distance

  • Claudio De Stefano
  • Marco Garruto
  • Luis Lapresa
  • Angelo Marcelli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)


Edit Distance has been widely studied and successfully applied in a large variety of application domains and many techniques based on this concept have been proposed in the literature. These techniques share the property that, in case of patterns having different lengths, a number of symbols are introduced in the shortest one, or deleted from the longest one, until both patterns have the same length. In case of applications in which strings are used for shape description, however, this property may introduce distortions in the shape, resulting in a distance measure not reflecting the perceived similarity between the shapes to compare. Moving from this consideration, we propose a new edit distance, called Weighted Edit Distance that does not require the introduction or the deletion of any symbol. Preliminary experiments performed by comparing our technique with the Normalized Edit Distance and the Markov Edit Distance have shown very encouraging results.


Machine Intelligence Edit Distance Curvature Maximum Segmentation Point Chinese Character Recognition 
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 2005

Authors and Affiliations

  • Claudio De Stefano
    • 1
  • Marco Garruto
    • 2
  • Luis Lapresa
    • 3
  • Angelo Marcelli
    • 2
  1. 1.Dipartimento di Automazione, Elettromagnetismo Ingegneria dell’Informazione e, Matematica IndustrialeUniversità di CassinoCassinoItaly
  2. 2.Dipartimento di Ingegneria dell’Informazione ed Ingegneria ElettricaUniversità di SalernoFiscianoItaly
  3. 3.Departament de Ciències Matemàtiques i InformàticaUniversitat de les Illes BalearsPalma (Illes Balears)Spain

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