Principal Curves And The Application To Chinese Characters Skeletonization

Principal curves are one-dimensional curves of data set in multidimensinal space. The theory and applicational study have been concerned with many computer researchers. Finding an effective algorithm of principal curves and studying its application have been the one point of animated thesis in pattern identify.Two parts compose this thesis. The first part expounds a self-adapted and concise principal curves algorithms. The second part is its application to Chinese character skeletonization. In the first part of my thesis, it above all designs an algorithm, called PCA, or picking up principal characteristic of data set. Based On it, a simpler principal curves algorithm is designed. The algorithm can be demarcated two segments. The first segment bases on the theory of generalized vector quantizer called refined GL_algorithm. It computes polygonal line principal curves for discrete data set. The other segment combines the method of self-organized topological mapping with refined GL_algorithm to design the principal curves algorithm. This principal curves algorithm inherits main virtues of the principal curves of HS-type and K-type, and it is simpler than HS-type and K-type. In the second part of my thesis, it is an application of the principal curves to Chinese characters skeletonization, including the characters skeletonization of handed type and any printing type. This part can be demarcated three layers. The first layer is called pretreatment. It is combined of many algorithms, including thinning Chinese characters, picking up the characteristic structure and segment of Chinese characters, creating stroke of Chinese characters . The second layer mostly includes the algorithms of optimizing stroke of Chinese characters. The third layer is to swith skeletonization of Chinese characters, to improve the principal curves algorithm, to optimize the characters skeletonization with principal curves, and to output the characters skeletonization.