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A Neural Network Model for Online Handwritten Mathematical Symbol Recognition

  • Arit Thammano
  • Sukhumal Rugkunchon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

Abstract

This paper proposes a new handwritten mathematical symbol recognition system that is flexible enough to let the users write the symbols in their own ways. They do not have to learn a completely new way of writing symbols. The proposed approach involves two main stages: online and offline. During the online stage, the input is classified into one of the four groups. During the offline stage, the new neural network, called Hausdorff ARTMAP, which is specifically designed for solving two dimensional binary pattern recognition problems is used to identify the symbols. The proposed model is tested in a writer independent mode using the researcher’s own collected database. The result obtained is very encouraging.

Keywords

Input Pattern Hausdorff Distance Cluster Node Stroke Position Mathematical Symbol 
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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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Arit Thammano
    • 1
  • Sukhumal Rugkunchon
    • 1
  1. 1.Computational Intelligence Laboratory Faculty of Information TechnologyKing Mongkut’s Institute of Technology LadkrabangBangkokThailand

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