Abstract
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 This paper presents a novel method for generating outline font from historical documents of Chinese calligraphy.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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wright, T.: History and Technology of Computer Fonts. IEEE Annals of the History of Computing 20(2), 30–34 (1998)
Apple Computer Inc., The TrueType Font Format Specication, Version 1.0. (1990)
Marji, M.: On the Detection of Dominant Points on Digital Planar Curves, PhD Thesis, Wayne State University, Detroit, Michigan (2003)
Rattangsi, A., Chin, R.: Scale-Based Detection of Corners of Planar Curves. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(4), 430–449 (1992)
Sezgin, T.M., Davis, R.: Scale-space Based Feature Point Detection for Digital Ink. In: Making Pen-Based Interaction Intelligent and Natural, pp. 145–151 (2004)
Rosenfeld, A., Johnston, E.: Angle Detection on Digital Curves. IEEE Transactions on Computers 22(9), 875–878 (1973)
Rosenfeld, A., Weszka, J.S.: An Improved Method of Angle Detection on Digital Curves. IEEE Transaction on Computers 24, 940–941 (1975)
Teh, C.H., Chin, R.T.: On the Detection of Dominant Points on Digital Curves. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(8), 859–872 (1989)
Witkin, A.P.: Scale-space Filtering. In: Proc. 8th Int. Joint Conf. Art. Intell., pp. 1019–1022 (1983)
Canny, J.: A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)
Weisstein, E.W.: Least Squares Fitting, http://mathworld.wolfram.com/LeastSquaresFitting.html
Sarfraz, M., Khan, M.: An Automatic Algorithm for Approximating Boundary of Bitmap Characters. Future Generation Computer Systems 20(8), 1327–1336 (2004)
Farin, G.: Curves and Surfaces for Computer Aided Geometric Design: A Practical Guide, 4th edn. Academic Press, New York (1997)
Zhang, J.S., Lin, H.W., Yu, J.H.: A Novel Method for Vectorizing Historical Documents of Chinese Calligraphy. In: IEEE International Conference on Computer-Aided Design and Computer Graphics, pp. 219–224. IEEE Press, Los Alamitos (2007)
Zhang, J.S., Yu, J.H., Lin, H.W.: Capturing Character Contours from Images of Ancient Chinese Calligraphy. In: IEEE 2nd Workshop on Digital Media and its Application in Museum & Heritage, pp. 36–41. IEEE Press, Los Alamitos (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Zhang, J., Mao, G., Lin, H., Yu, J., Zhou, C. (2011). Outline Font Generating from Images of Ancient Chinese Calligraphy. In: Pan, Z., Cheok, A.D., Müller, W., Yang, X. (eds) Transactions on Edutainment V. Lecture Notes in Computer Science, vol 6530. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18452-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-18452-9_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-18451-2
Online ISBN: 978-3-642-18452-9
eBook Packages: Computer ScienceComputer Science (R0)