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Segmentation of On-Line Handwritten Japanese Text Using SVM for Improving Text Recognition

  • Bilan Zhu
  • Junko Tokuno
  • Masaki Nakagawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3872)

Abstract

This paper describes a method of producing segmentation point candidates for on-line handwritten Japanese text by a support vector machine (SVM) to improve text recognition. This method extracts multi-dimensional features from on-line strokes of handwritten text and applies the SVM to the extracted features to produces segmentation point candidates. We incorporate the method into the segmentation by recognition scheme based on a stochastic model which evaluates the likelihood composed of character pattern structure, character segmentation, character recognition and context to finally determine segmentation points and recognize handwritten Japanese text. This paper also shows the details of generating segmentation point candidates in order to achieve high discrimination rate by finding the combination of the segmentation threshold and the concatenation threshold. We compare the method for segmentation by the SVM with that by a neural network using the database HANDS-Kondate_t_bf-2001-11 and show the result that the method by the SVM bring about a better segmentation rate and character recognition rate.

Keywords

Support Vector Machine Testing Pattern Character Recognition Training Pattern Text Line 
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

  • Bilan Zhu
    • 1
  • Junko Tokuno
    • 1
  • Masaki Nakagawa
    • 1
  1. 1.Tokyo University of Agriculture and TechnologyTokyoJapan

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