A multiple classifier approach to detect Chinese character recognition errors

Authors:
K. -Y. Hung;R. W. P. Luk;D. S. Yeung;K. F. L. Chung;W. Shu
Affiliations:
Department of Computing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, PR China;Department of Computing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, PR China and Centre for Multimedia Signal Processing, the Hong Kong Polytechnic University, Hong Kong, PR C ...;Department of Computing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, PR China;Department of Computing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, PR China and Centre for Multimedia Signal Processing, the Hong Kong Polytechnic University, Hong Kong, PR C ...;Department of Computing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, PR China and Department of Computer Science and Engineering, Harbin Institute of Technology, PR China
Venue:
Pattern Recognition
Year:
2005

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Abstract

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.