Outline Font Generating from Images of Ancient Chinese Calligraphy

Chinese calligraphy is an art unique to Asian cultures. This paper presents a novel method for generating outline font from historical documents of Chinese calligraphy. The method consists of detecting feature points from character boundaries, and approximating contour segments. The feature-point-detection is based on statistical method considering the characteristics of a calligrapher. A database of basic strokes and some overlapping stroke components of Chinese characters extracted from the calligrapher are constructed in advance. And the relation between the noise level of stroke contours and the standard deviation of Gaussian kernel is retrieved from the database using linear regression. Thus, given an input character contour, the standard deviation for smoothing the noisy character contour can be calculated. Furthermore, a new method is employed to determine the feature points at the standard deviation. The feature points at a character contour subdivide the contour into segments. Each segment can be fitted by a parametric curve to obtain the outline font. Some experimental results and the comparisons to existing methods are also presented in the paper.