DocumentCode
3019585
Title
Study on Chinese handwriting identification based on texture analysis
Author
Feng, Jun ; Gao, Xu
Author_Institution
Sch. of Comput. & Inf. Eng., Shijiazhuang Inst. of Railway, Shijiazhuang, China
fYear
2009
fDate
12-15 July 2009
Firstpage
215
Lastpage
219
Abstract
As a kind of behavior-based personal identification techniques, automated Chinese handwriting identification becomes a hot topic in pattern recognition and machine learning research area. There are lots of key issues worthy researching. In this paper, the Chinese handwriting identification technology based on texture analysis is discussed. Firstly, a practical Chinese handwriting image samples library CHSL2007 is established for the comparison of exist algorithms and further research. Then the methods of feature extraction based on texture analysis are explored and the pairwise SVM classifier is utilized. The experiment results of texture analysis based on Gabor filter is compared with DB6 wavelet filter and demonstrate that the former is more suitable for handwriting identification on CHSL2007. Finally, the sheet recognition rate is defined and can be arrived at above 99.50% for CHSL2007.
Keywords
feature extraction; handwriting recognition; image classification; image texture; learning (artificial intelligence); natural languages; support vector machines; Chinese handwriting identification; behavior-based personal identification technique; feature extraction; machine learning; pairwise SVM classifier; pattern recognition; texture analysis; Feature extraction; Gabor filters; Image texture analysis; Libraries; Machine learning; Machine learning algorithms; Pattern recognition; Support vector machine classification; Support vector machines; Wavelet analysis; Chinese handwriting samples library; Handwriting identification; SVM classifier; Texture feature analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Wavelet Analysis and Pattern Recognition, 2009. ICWAPR 2009. International Conference on
Conference_Location
Baoding
Print_ISBN
978-1-4244-3728-3
Electronic_ISBN
978-1-4244-3729-0
Type
conf
DOI
10.1109/ICWAPR.2009.5207424
Filename
5207424
Link To Document