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
Link To Document :
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