%0 Journal Article
%T Recognition of a Limited Chinese Character Set Based on PCA Learning Subspace Algorithm<br>基于PCA学习子空间算法的有限汉字识别
%A JIANG Wei feng
%A LIU Ji lin
%A <br>蒋伟峰
%A 刘济林
%J 中国图象图形学报
%D 2001
%I 
%X This paper is to realize the optical character recognition on grey scale level by adopting learning subspace method of principal component analysis(PCALSM). Compared with Arabic number images, the resolution of Chinese character images is small, which creates great difficulty in extracting the character features. And it will get worse especially when the quality of image is low. PCALSM can overcome the main shortages of classification on binary images, and keeps integrity features of character information dramatically. On the basis of PCA subspaces, training of each subspace is rotated in different ways of the supervised feedback learning algorithm; and better classification is therefore obtained. The time consuming subspace training can be accepted especially when the number of character classes is not large. Our experimental results have proved that recognition of car license plate characters (a limited Chinese character set) has been improved by PCALSM, which makes it highly worth applying this optical character recognition (OCR) method.
%K Gray
%K scale character recognition
%K PCALSM
%K Image information features<br>灰度图象
%K OCR识别
%K PCA学习子空间算法
%K 字符特征信息
%K 有限汉字识别
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=733056D10B2D6CF5&yid=14E7EF987E4155E6&vid=B31275AF3241DB2D&iid=0B39A22176CE99FB&sid=50BBDFAC8381694B&eid=6235172E4DDBA109&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=10&reference_num=5