Coarse classification of Chinese characters via stroke clustering method

Chin Chuan Han, Yao Lung Tseng, Kuo Chin Fan, An Bang Wang

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


In this paper, we propose a stroke clustering-based coarse classification mechanism to classify the multi-fonts Chinese characters. The main purpose of the proposed method is to identify the associating type of an input character together with the extraction of its embedded composing components. In this paper, the K-mean clustering algorithm is employed to cluster the thinned strokes. Besides, mis-clustered stroke modification techniques are developed to rearrange the mis-clustered strokes generated by the K-mean algorithm. Five kinds of fonts for 2500 frequently used Chinese characters are tested in our experiments. The average classification rate is 92.57% which is very promising for coarse classification.

Original languageEnglish
Pages (from-to)1079-1089
Number of pages11
JournalPattern Recognition Letters
Issue number10
StatePublished - Oct 1995


  • Coarse classification
  • Dividing path
  • K-mean clustering
  • Mis-stroke modification


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