Offline recognition of chinese handwriting by multifeature and multilevel classification

Yuan Y. Tang*, Lo Ting Tu, Jiming Liu, Seong Whan Lee, Win Win Lin, Ing Shyh Shyu

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

79 Citations (Scopus)


One of the most challenging topics is the recognition of Chinese handwriting, especially offline recognition. In this paper, an offline recognition system based on multifeature and multilevel classification is presented for handwritten Chinese characters. Ten classes of multifeatures, such as peripheral shape features, stroke density features, and stroke direction features, are used in this system. The multilevel classification scheme consists of a group classifier and a five-level character classifier, where two new technologies, overlap clustering and Gaussian distribution selector, are developed. Experiments have been conducted to recognize 5,401 daily-used Chinese characters. The recognition rate is about 90 percent for a unique candidate, and 98 percent for multichoice with 10 candidates.

Original languageEnglish
Pages (from-to)556-561
Number of pages6
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number5
Publication statusPublished - May 1998

Scopus Subject Areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

User-Defined Keywords

  • Gaussian distribution selector
  • Multifeature
  • Multilevel classification
  • Offline chinese handwriting recognition
  • Overlap clustering


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