Similar articles

Abstract: In this paper, a novel approach is proposed for denoising of Chinese calligraphy tablet documents. The method includes two phases: First, a partial differential equations (PDE) based the total variation model and Otsu thresholding method are used to preprocess the calligraphy document image. Second, a new method based on run-length statistics and structure characteristics of Chinese characters is proposed to remove some random and ant-like noises. This includes the optimal threshold selection from histogram of run-length probability density, and improved Hough transform algorithm for line shape noise detection and removal. Examples are given in the paper to demonstrate the proposed method.

[1] Chan, T.F., Esedoglu, S., Nikolova, M., 2004. Algorithms for Finding Global Minimizes of Image Segmentation and Denoising Models. UCLA CAM Report 04-54.

[2] Chu, N.S.H., Tai, C.L., 2005. MoXi: Real-time Ink Dispersion in Absorbent Paper. ACM Trans. on Graphics (SIGGRAPH’05), 24(3):504-511.

[3] Fan, K.C., Wu, W.H., 2000. A Run-length Coding Based Approach to Stroke Extraction of Chinese Characters. 15th International Conference on Pattern Recognition (ICPR’00), 2:565-568.

[4] Girshick, R.B., 2004. Simulating Chinese Brush Painting: The Parametric Hairy Brush. Proc. of SIGGRAPH’04.

[5] Gonzalez, R.C., Woods, R.E., 2003. Digital Image Processing, 2nd Edition. Publishing House of Electronics Industry, Beijing.

[6] Guo, Q., Kunii, T., 1991. Modelling the diffuse painting of “Sumi-e”. Modelling in Computer Graphics. Springer-Verlag, Tokyo, p.329-338.

[7] Lee, J., 1999. Simulating oriental black-ink painting. IEEE Computer Graphics and Applications, 19(3):74-81.

[8] Lee, J., 2001. Diffusion rendering of black ink paintings using new paper and ink models. Computers & Graphics, 25(2):295-308.

[9] Mi, X.F., Xu, J., Tang, M., Dong, J.X., 2002. The Droplet Virtual Brush for Chinese Calligraphic Character Modelling. Sixth IEEE Workshop on Applications of Computer Vision. Orlando, Florida, p.330-338.

[10] Otsu, N., 1979. A threshold selection method from gray level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1):62-66.

[11] Rudin, L.I., Osher, S., Fatemi, E., 1992. Nonlinear total variation based noises removal algorithms. Physica D Nonlinear Phenomena, 60(1-4):259-268.

[12] Sam, T.S., Wong, H.L., Ip, H.H.S., 2005. Model-based Analysis of Chinese Calligraphy Images. 9th International Conference on Information Visualization. London, England, p.221-226.

[13] Strassmann, S., 1986. Hairy brushes. Computer Graphics (SIGGRAPH’86), 20(4):225-232.

[14] Wang, S.Z., Lee, H.J., 2001. Dual-Binarization and Anisotropic Diffusion of Chinese Characters in Calligraphy Documents. Proceedings of the Sixth International Conference on Document Analysis and Recognition, p.271-275.

[15] Way, D.L., Huang, S.W., Shih, Z.C., 2003. Physical-based Model of Ink Diffusion in Chinese Ink Paintings. WSCG, 2003.

[16] Wong, H., Ip, H., 2000. Virtual brush: a model-based synthesis of Chinese calligraphy. Computer & Graphics, 24(1):99-113.

[17] Xu, S.H., Tang, M., Lau, F., Pan, Y.H., 2002. A solid model based virtual hairy brush. Computer Graphics Forum, 21(3):299-308.

[18] Xu, S.H., Lau, F.C.M., Cheung, W.K., Pan, Y.H., 2005. Automatic generation of artistic Chinese calligraphy. IEEE Intelligent Systems, 20(3):32-39.

[19] Ye, X.Y., Cheriet, M., Suen, C.Y., 2001. Stroke-model-based character extraction from gray-level document images. IEEE Trans. Image Processing, 10(8):1152-1161.

[20] Yu, J.H., Peng, Q.S., 2005. Realistic synthesis of Cao shu of Chinese calligraphy. Computers & Graphics, 29(1):145-153.

[21] Zhang, S.Z., 2003. Statistic Characteristics of Strokes of Chinese Characters. Http://www.chancezoo.net/hz/hzbhtjtx.htm.

[22] Zhuang, Y.T., Zhang, X.F., Wu, J.Q., Lu, X.Q., 2004. Retrieval of Chinese calligraphic character image. PCM, (1):17-24.