A multiple classifier approach to detect Chinese character recognition errors

✍ Scribed by K.-Y. Hung; R.W.P. Luk; D.S. Yeung; K.F.L. Chung; W. Shu

• Publisher: Elsevier Science
• Year: 2005
• Tongue: English
• Weight: 481 KB
• Volume: 38
• Category: Article
• ISSN: 0031-3203
• DOI: 10.1016/j.patcog.2004.09.004

✦ Synopsis

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.