Offline recognition of handwritten Chinese characters using gabor features, CDHMM modeling and MCE training

Yong Ge, Qiang Huo, Zhi Dan Feng

Research output: Contribution to journalConference articlepeer-review

16 Scopus citations


We've been developing a Chinese OCR engine for handwritten Chinese scripts. Currently, our OCR engine supports a vocabulary of 4616 characters which include 4516 simplified Chinese characters in GB2312-80, 62 alphanumeric characters, 38 punctuation marks and symbols. By using 1,384,800 character samples to train our recognizer, an averaged character recognition accuracy of 96.34% is achieved on a testing set of 1,025,535 character samples. An arguably best Chinese OCR product on the market achieves an accuracy of 94.07% for the recognizable Chinese characters in the above testing set. In this paper, we describe key techniques used in our recognizer that contribute to the high recognition accuracy, namely the use of Gabor features and their spatial derivatives as raw features, the use of LDA for feature extraction and dimension reduction, the use of CDHMMs for modeling Chinese characters along both horizontal and vertical directions, and the use of minimum classification error as a criterion for model training.

Original languageEnglish (US)
Pages (from-to)I/1053-I/1056
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - 2002
Externally publishedYes
Event2002 IEEE International Conference on Acustics, Speech, and Signal Processing - Orlando, FL, United States
Duration: May 13 2002May 17 2002

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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