Effective Handwritten Hangul Recognition Method Based on the Hierarchical Stroke Model Matching

  • Wontaek Seo
  • Beom-joon Cho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4109)


This study defines three models based on the stroke for handwritten Hangul recognition. Those are trainable and not sensitive to variation which is frequently founded in handwritten Hangul. The first is stroke model which consists of 32 stroke models. It is a stochastic model of stroke which is fundamental of character. The second is grapheme model that is a stochastic model using composition of stroke models and the last is character model that is a stochastic model using relative locations between the grapheme models. This study also suggests a new stroke extraction method from a grapheme. This method does not need to define location of stroke, but it is effective in terms of numbers and kinds of stroke models extracted from graphemes of similar shape. The suggested models can be adapted to hierarchical bottom-up matching, that is the matching from stroke model to character model. As a result of experiment, we obtain 88.7% recognition rate of accuracy that is better than those of existing studies.


Grapheme Model Recognition Rate Random Graph Character Model Matching Probability 
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 2006

Authors and Affiliations

  • Wontaek Seo
    • 1
    • 2
  • Beom-joon Cho
    • 1
    • 2
  1. 1.Dept. of Computer ScienceUniversity of MarylandCollege Park
  2. 2.Dept. of Computer EngineeringChosun UniversityGwangjuKorea

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