Chinese Character Component Deformation Based on AHP
Source abstract
Abstract
Since Chinese characters are composed of components, deforming the components in a small number of existing calligraphy characters to generate new characters is an effective method to produce a Chinese character library in the same style. Usually, the component deformation is achieved by affine transformation. However, when calculating the parameters in affine transformation, existing methods usually have the problems of a large amount of manual participation or complicated calculation. In this paper, we proposed an Analytic Hierarchy Process (AHP)-based Chinese character component deformation method, which is simple in calculation and can effectively realize the deformation of Chinese character components on the basis of reducing manual intervention. We first determined the factors that affect the selection of control points in affine transformation, then used AHP to calculate the weights of feature points and select the control points according to the weights. As a prerequisite for affine transformation, a matching method of Chinese character feature points based on the Chinese character skeleton map and neighborhood information is also proposed, which helps to achieve more efficient deformation. Experimental results on different fonts demonstrate the effectiveness and generality of our method.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Image and Video Retrieval Techniques · Image Retrieval and Classification Techniques · Handwritten Text Recognition Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Cyber Security and Computer, Hebei University, Baoding 071002, China
Hebei Machine Vision Engineering Research Center, Hebei University, Baoding 071002, China
Funding
Science and Technology Project of Hebei Education Department · “one province, one university” fund of Hebei University
References (28)
Show source-provided references
1. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8–13). Generative adversarial nets. In Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada.
2. Kaji, S., and Kida, S. (2019, June 10). Overview of Image-to-Image Translation Using Deep Neural Networks: Denoising, Super-Resolution, Modality-Conversion, and Reconstruction in Medical Imaging. Available online: https://kyushu-u.pure.elsevier.com/ja/publications/overview-of-image-to-image-translation-by-use-of-deep-neural-netw.10.1007/s12194-019-00520-y ↗
3. A restoration method using dual generate adversarial networks for Chinese ancient characters10.1016/j.visinf.2022.02.001 ↗
4. Tian, Y. (2017, April 06). zi2zi: Master Chinese Calligraphy with Conditional Adversarial Networks. Available online: https://github.com/kaonashi-tyc/zi2zi/.
5. Radical analysis network for learning hierarchies of Chinese characters10.1016/j.patcog.2020.107305 ↗
6. Zero-shot Handwritten Chinese Character Recognition with hierarchical decomposition embedding10.1016/j.patcog.2020.107488 ↗
7. Ministry of Education of the People’s Republic of China (2009). GF 0014-2009 Specification of Common Modern Chinese Character Components and Component Names.
8. Hierarchical Chinese character database based on radical reuse
9. Zu, X., and Jin, L. (2008). Hierarchical Chinese Character Database Based on Global Affine Transformation, South China University of Technology.
10. Derpanis, K.G. (2004). The Harris Corner Detector, York University.
11. Shi, J. (1994, January 21–23). Good features to track. In Proceedings of the 1994 IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.
12. Distinctive image features from scale-invariant keypoints10.1023/B:VISI.0000029664.99615.94 ↗
13. Bay, H., Tuytelaars, T., and Gool, L.V. (2006, January 7–13). SURF: Speeded up robust features. Proceedings of the 9th European Conference on Computer Vision, Graz, Austria.10.1007/11744023_32 ↗
14. Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011, January 6–13). Orb: An efficient alternative to sift or surf. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.10.1109/ICCV.2011.6126544 ↗
15. Bian, J., Lin, W., Matsushita, Y., Yeung, S., Nguyen, T., and Cheng, M. (2017, January 21–26). GMS: Grid-Based Motion Statistics for Fast, Ultra-Robust Feature Correspondence. Proceedings of the IEEE Conference on Computer Vision & Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.302 ↗
16. Morphing technology for Chinese characters based on skeleton graph matching
17. Research on distortionless resizing method for components of Chinese characters
18. Research on Chinese Characters Described by Track and Point Sets Changing from Regular Script to Semi-Cursive Scrip
19. Lian, Z., and Xiao, J. (December, January 28). Automatic Shape Morphing for Chinese characters. Proceedings of the SIGGRAPH Asia 2012 Technical Briefs, Singapore.10.1145/2407746.2407748 ↗
20. Automatical System to Generate High-Quality Chinese Font Libraries Based on Component Assembling
21. Parametric Estimation of Affine Transformations: AnExactLinear Solution10.1007/s10851-009-0188-4 ↗
22. Combined Affine Geometric Transformations and Spatially Dependent Radiometric Deformations: A Decoupled Linear Estimation Framework10.1109/TIP.2011.2136353 ↗
23. Generalized centroids with applications for parametric estimation of affine transformations
24. Schaefer, S., McPhail, T., and Warren, J. (2006). Image deformation using moving least squares. ACM SIGGRAPH 2006 Papers (SIGGRAPH ’06), Association for Computing Machinery.10.1145/1179352.1141920 ↗
25. How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy10.1016/j.omega.2020.102261 ↗
26. Saaty, T.L. (1980). The Analytic Hierarchy Process, McGraw-Hill.10.21236/ADA214804 ↗
27. GUO Dongdong, SONG Tianbao. Quantitative Analysis of Chinese Vocabulary Comprehensice Complexity Base on AHP
28. Calculation on High-ranked R I of Analytic Hierarchy Process