Using semantics in matching cursive Chinese handwritten annotations

  • Matthew Y. Ma
  • Patrick S. P. Wang
Handwritten Character Recognition
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)


We propose a semantic matching network for the matching of cursive Chinese handwritten annotations. This architecture combines the semantics of Chinese language with the traditional elastic ink matching. Using semantics can make the matching algorithm more intelligent by pre-selecting the most likely candidates before elastic ink matching is applied thus speed up the whole matching process. The semantic matching network can also establish a link between Chinese handwritten annotations and typed text, which can be used to match between these two. Our experiments show that 75 – 85% recall can be achieved with a speed improvement of 85% over traditional elastic ink matching.


Edit Distance Radical Extraction Radical Code Handwriting Recognition Semantic Match 
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 1998

Authors and Affiliations

  • Matthew Y. Ma
    • 1
  • Patrick S. P. Wang
    • 2
  1. 1.Panasonic Information and Networking Technologies LaboratoryPanasonic Technologies, Inc.PrincetonUSA
  2. 2.College of Computer ScienceNortheastern UniversityBostonUSA

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