A multiple classifier approach to detect Chinese character recognition errors

• Published in: Pattern recognition 2005-05, Vol.38 (5), p.723-738
• Main Authors: Hung, K.-Y., Luk, R.W.P., Yeung, D.S., Chung, K.F.L., Shu, W.
• Format: Article
• Language: English
• Subjects: Character recognition; Error detection; Pattern recognition and language modeling
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2004.09.004

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.