A multiple classifier approach to detect Chinese character recognition errors

K. Y. Hung, Wing Pong Robert Luk, Daniel S. Yeung, Fu Lai Korris Chung, Wenhao Shu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)


Detection of recognition errors is important in many areas, such as improving recognition performance, saving manual effort for proof-reading and post-editing, and assigning appropriate weights for retrieval in constructing digital libraries. We propose a novel application of multiple classifiers for the detection of recognition errors. A need for multiple classifiers emerges when a single classifier cannot improve recognition-error detection performance compared with the current detection scheme using a simple threshold mechanism. Although the single classifier does not improve recognition error performance, it serves as a baseline for comparison and the related study of useful features for error detection suggests three distinct cases where improvement is needed. For each case, the multiple classifier approach assigns a classifier to detect the presence or absence of errors and additional features are considered for each case. Our results show that the recall rate (70-80%) of recognition errors, the precision rate (80-90%) of recognition error detection and the saving in manual effort (75%) were better than the corresponding performance using a single classifier or a simple threshold detection scheme.
Original languageEnglish
Pages (from-to)723-738
Number of pages16
JournalPattern Recognition
Issue number5
Publication statusPublished - 1 May 2005


  • Character recognition
  • Error detection
  • Pattern recognition and language modeling

ASJC Scopus subject areas

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


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