基于PCA学习子空间算法的有限汉字识别

采用PCA学习子空间方法来进行灰度图象上字符的识别，不仅克服了传统的基于二值化字符特征提取和识别所带来的主要困难，还尽量多地保存了字符特征，该算法在PCA子空间的基础上，通过反馈监督学习的方法使子空间作旋转调整，从而获得了更好的分类效果，特别当字符类别数不是很大时，子空间的训练时间也将在可接受的范围之内，应用效果也表明，采用PCAA学习子空间算法对车牌汉字这一有限汉字集进行识别，取得了较好的效果，实用价值较高。

Recognition of a Limited Chinese Character Set Based on PCA Learning Subspace Algorithm

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.