ootaya.blogg.se

Chinese paintbrush font translate
Chinese paintbrush font translate






chinese paintbrush font translate

for Pattern Recognition, Hague, Netherlands, pp. 11th International Conference on Pattern Recognition, Assoc. Tan, T.N.: Texture Feature Extraction via Cortical Channel Modeling. In: Proceedings of the First International Conference on Machine Learning and Cybernetics, Beijing, China, pp. Yang, F., Tian, X.D.: An Improved Font Recognition Method Based on Texture Analysis. Tian, X.D., Guo, B.L.: Chinese Character Font Recognition Based on Texture Features. IEEE transactions on Evolutionary Computation 2, 164–171 (2000) Raymer, M.L., Punch, W.F.: Dimensionality Reduction Using Genetic Algorithm. National Defence Industry Press, Beijing (1999) Zhou, M., Sun, S.D.: Genetic Algorithm: Theory and Applications. In: Proceeding of SPIE, Visual Communications and Image Processing, vol. 2501, pp. Patel, D., Stonham, T.J.: Accurate Set-up of Gabor Filters for Texture Classification.

chinese paintbrush font translate

Jain, A.K., Farrokhnia, F.: Unsupervised Texture Segmentation Using Gabor Filters.

chinese paintbrush font translate

IEEE transactions on Pattern Analysis and Machine Intelligence 1, 55–73 (1990) Image and Vision Computing 19, 329–338 (2001)īovik, A.C., Clark, M., Geisler, W.S.: Multichannel Texture Analysis Using Localized Spatial Filters. Lin, C.F., Fang, Y.F., Juang, Y.T.: Chinese Text Distinction and Font Identification by Recognizing Most Frequently Used Characters. 353–356 (1999)Ĭhang, C.H., Chen, C.D.: A Study on Corpus-based Classification of Chinese Words. of International Conference on Document Analysis and Recognition, Bangalore, India, pp. Jung, M.C., Shin, Y.C., Srihari, S.N.: Multifont Classification Using Typographical Attributes. IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 877–882 (1998) Zramdini, R.: Optical Font Recognition Using Typographical Features. (ed.) Progress of Intelligence Computer Research, pp. Pattern Recognition Letters 26, 135–145 (2005)Ĭhen, L., Ding, X.Q.: Font Recognition of Single Chinese Character. Pattern Analysis and Machine Intelligence 10, 1192–1200 (2001)Ĭarlos, A.C., Risto, R.K., Mario, R.A.: High-order Statistical Texture Analysis–Font Recognition Applied. Zhu, Y., Tan, T.N., Wang, Y.H.: Font Recognition Based on Global Texture Analysis. In: Proceedings of International Conference on Computer Vision, Graphics and Image Processing, Taiwan, pp. 811–814 (1995)įan, K.C., Wang, L.S.: A Run Length Histogram Based Approach to the Identification of Machine-Printed and Handwritten Chinese Text Images. In: Proceedings of the International Conference on Document Analysis and Recognition, Montreal, Canada, vol. 2, pp. Kuhnke, K., Simoncini, L., Kovacs-V, Z.M.: A System for Machine-written and Hand-written Character Distinction. Computer Vision and Image Understanding 1, 66–74 (1996) Khoubyari, S., Hull, J.J.: Font and Function Word Identification in Document Recognition. This process is experimental and the keywords may be updated as the learning algorithm improves. These keywords were added by machine and not by the authors. Experiments are carried out and the results show that this method is of immense practical and theoretical value. Finally, a post-processing is fulfilled according to the layout knowledge to correct the errors of font recognition. Then a font recognizer is run to identify the font of the characters one by one. First, the guiding fonts are acquired based on Gabor features. It employs a statistical method based on global texture analysis to recognize a predominant font, and uses a traditional recognizer of a single font to identify the font of a single character by the guidance of an obtained predominant font. In this paper, a new method of optical font recognition is proposed which could recognize the font of every Chinese character. Font recognition is a fundamental issue in the identification, analysis and reconstruction of documents.








Chinese paintbrush font translate