DocumentCode
2622821
Title
A printed Chinese character recognition method
Author
Hu, Xiaobing ; Peng, Junjie ; Wang, MinChao ; Shen, Rong ; Huang, Kanrun ; Chen, Chang
Author_Institution
Sch. of Comput. Sci. & Eng., Shanghai Univ., Shanghai, China
fYear
2011
fDate
27-29 June 2011
Firstpage
2904
Lastpage
2907
Abstract
Chinese character recognition is integrated technology that is related to pattern recognition, artificial intelligence, fuzzy mathematics, information theory, computer science and so on. Currently a lot of researches focused on the Chinese character recognition have been done, however, the results are still not very satisfactory. In this paper, A new recognition method is put forward based on the previous researches in this field and the combination of the statistical classification and neural networks. Using neural network to implement vector conversion and thus achieve the recognition of text, the method not only avoids the interference characteristics of Chinese structures, but also much improved the Chinese character recognition rate. Experiments with a large number of training samples show that the method of Chinese character recognition rate with the proposed method is more than 93%.
Keywords
character recognition; image recognition; neural nets; statistical analysis; Chinese structures; artificial intelligence; computer science; fuzzy mathematics; information theory; interference characteristics; neural networks; pattern recognition; printed Chinese character recognition; statistical classification; text recognition; vector conversion; Artificial neural networks; Character recognition; Computer science; Image recognition; Publishing; Technological innovation; BP neural network; formatting; image binarization; insert (key words) Character recognition; style; styling; text refinement;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Service System (CSSS), 2011 International Conference on
Conference_Location
Nanjing
Print_ISBN
978-1-4244-9762-1
Type
conf
DOI
10.1109/CSSS.2011.5974803
Filename
5974803
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