# Chinese Character Component Deformation Based on AHP
## 1 Introduction  
Currently, with the widespread application of computer fonts, there are many types of online Chinese character libraries. More and more people prefer to communicate with others in their preferred writing style rather than a uniform printing style, such as some personal handwriting fonts, traditional calligraphy fonts, etc. However, there are 3500 commonly used characters, and the official standard GB2312 for the Chinese character set has a total of 6763 Chinese characters. It is almost impossible for a calligrapher to write so many Chinese characters in the same style correctly. Therefore, how to automatically generate more characters from a small number of existing Chinese characters on the basis of retaining the original writing style has attracted the interest of many researchers. One possible way is the utilization of deep learning techniques. In recent years, deep learning has performed well in style transfer and image generation [1; 2]. Many people use it to solve Chinese character problems, usually by learning the writing style of the target font to convert the print font into the target font [3; 4]. However, the characters obtained by such methods often differ from the real ones in structure and style. That is mainly because deep learning-based approaches are good at the transfer of image texture and color, but Chinese characters have complex structures. Even small changes in the location and geometry of their elements (e.g., components, and strokes) can dramatically change their meanings or styles. A valuable characteristic of Chinese characters is that they are highly hierarchical and a lot of them are composed of basic components [5; 6]. It is worth noting that the number of Chinese character components is far less than the number of Chinese characters. According to [7], there are only 514 components segmented from 3500 commonly used characters. As shown in Figure 1, the same component can form different Chinese characters. Anotherefficient way to solve the problem is to deform the existing components to generate new components that can be used to compose a new character. It can also be seen from Figure 1 that the shape of the same component varies with its position in Chinese characters, which is usually realized by an affine transformation. This is mainly because in the deformation of Chinese character components, it is necessary to ensure the fluency of strokes and the invariance of structure, and affine transformation has the property of ensuring the straightness and parallelism of images. However, a common difficulty we often encounter while manipulating affine transformations is how to calculate the parameters. Existing methods usually have a large amount of manual participation or complicated calculations. For example, [PERSON] et al. [8] proposed a component deformation method based on affine transformation, but the control points need to be selected manually, and the amount of manual participation is large. [PERSON] et al. [9] calculated affine transformation parameters by constructing nonlinear equations, but the result may have multiple solutions, which need to be verified one by one. In order to achieve a more efficient and high-quality deformation of Chinese character components, we proposed an Analytic Hierarchy Process (AHP)-based affine transformation parameter calculation method, which can effectively realize the deformation of Chinese character components on the basis of reducing manual intervention and simple calculation. Both qualitative and quantitative results in different fonts prove that the method we proposed can achieve a better effect. The overall procedure is shown in Figure 2.  
Figure 1: One component can be deformed to form different Chinese characters. Figure 2: The overall procedure of our experiment. The input consists of an original component image and a reference component image. The original component is a handwritten font, and the reference component is a regular print font that provides a reference for the deformation of the original component.