This paper presents an innovative method to align two glyph contours with three steps. First, 2D Bézier curve control points of glyph contours of each character are expanded into 3D space. Second, a Gaussian Mixture Model (GMM) is constructed using this 3D point set. Finally, the authors establish alignment by minimizing the Euclidean Distance (L2) between two GMMs and then apply transformation accordingly. Expansion to 3D space helps make use of inherent constraints of Chinese calligraphy beyond 2D coordinates. The advantage of using Gaussian Mixture Model is to maintain both the overall shape property and the local writing features during the alignment process. Experiments results verify the feasibility and effectiveness of proposed method and it performs well for both single stroke and whole character.

For intelligent teaching of Chinese character handwriting, the authors present a stroke retrieval method for handwritten Chinese character images, which includes three steps. Firstly, the method extracts the skeletons from the handwritten image. Secondly, from the perspective of knowledge engineering, it eliminates the skeleton distortions by using the stable grapheme topology. Thirdly, it divides the skeletons into some strokes and outputs the matching relationship between them and the strokes in the template character, by building and solving the similarity model with A* algorithm. The result of the method can be used to automatic quality assessment for handwritten Chinese character images.

The distribution and meaning of natural annotations on large datasets are discussed. The proposed research on word extraction shows the positive potential of both implicit and explicit natural annotation in word segmentation. Experiments on word extraction indicates that the implicit natural annotation derived from language laws and patterns are more powerful in splitting character strings in raw corpora.