A stochastic representation of cursive Chinese characters for on-line recognition

Tzren Ru Chou, Wen Tsuen Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


In this paper, a stochastic representation of on-line Chinese characters of cursive style is proposed. A character in this representation is modeled by a sequence of concatenated stochastic curves, termed stochastic cubic Bézier curves (SCBC), with random noises. Furthermore, we also propose a curve alignment procedure to consistently match an input character with a stochastic reference one. Some classification experiments were performed. The stochastic approach is hardly sensitive to the characters with linked and degraded strokes; meanwhile, its recognition rate is higher than 95% even for deformed confusing characters.

Original languageEnglish
Pages (from-to)903-920
Number of pages18
JournalPattern Recognition
Issue number6
Publication statusPublished - 1997 Jun
Externally publishedYes


  • Bézier curves
  • De Casteljau algorithm
  • Dynamic programming
  • Elastic matching
  • Mahalanobis distance
  • Maximum likelihood estimation
  • On-line character recognition

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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