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Multi-class character classification with semi-supervised learning based on information entropy
Xue Wei Wang1; Yuan Yan Tang1; Jia Duan1; Liang Rui Peng2; Zhen Chao Zhang1
2013-09-03
Conference Name2013 2nd International Symposium on Electrical & Electronics Engineering (EEESYM 2013)
Source PublicationApplied Mechanics and Materials
Volume347-350
Pages3167-3171
Conference Date2013
Conference PlaceShijiazhuang
Abstract

Character Classification technology is the key link in OCR system. Most classification methods require abundant marked samples training to get classifier. In the real OCR application, there are so many classes, to label these samples are often waste time and energy, especially for unacquainted language, such as Arabic and Uygur, many characters are difficult to differentiate, so it even needs the help of professional guidance. This paper proposed a novel character classification with semi-supervised learning based on information entropy, introduced discrete event probability estimation theory of information entropy, active to select the optimization character samples, got the new parameters to train the classifier again, choose the most conducive to the classifier performance samples, iteration until the unlabeled samples set is empty. The experiment results show that this method achieves high performance in specific condition. © 2013 Trans Tech Publications Ltd, Switzerland.

KeywordArabic Ocr Character Classification Information Entropy Semi-supervised
DOIhttps://doi.org/10.4028/www.scientific.net/AMM.347-350.3167
URLView the original
Language英语
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Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, China
2.Department of Electronic Engineering of Tsinghua University,Beijing,100083,China
First Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Xue Wei Wang,Yuan Yan Tang,Jia Duan,et al. Multi-class character classification with semi-supervised learning based on information entropy[C],2013:3167-3171.
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