This paper addresses the automatic generation of a typographic font from a subset of characters. Specifically, we use a subset of a typographic font to extrapolate additional characters. Consequently, we obtain a complete font containing a number of characters sufficient for daily use. The automated generation of Japanese fonts is in high demand because a Japanese font requires over 1,000 characters. Unfortunately, professional typographers create most fonts, resulting in significant financial and time investments for font generation. The proposed method can be a great aid for font creation because designers do not need to create the majority of the characters for a new font. The proposed method uses strokes from given samples for font generation. The strokes, from which we construct characters, are extracted by exploiting a character skeleton dataset. This study makes three main contributions: a novel method of extracting strokes from characters, which is applicable to both standard fonts and their variations; a fully automated approach for constructing characters; and a selection method for sample characters. We demonstrate our proposed method by generating 2,965 characters in 47 fonts. Objective and subjective evaluations verify that the generated characters are similar to handmade characters.
* Font blending methods Methods in this category receive several complete fonts and generate a font by blending them. Xu generated Chinese calligraphy characters using a weighted blend of strokes in different styles @cite_15 . They decomposed samples into radicals and single strokes based on rules defined by expert knowledge. An improved method @cite_37 considers the spatial relationship of strokes. Choi generated handwriting Hangul characters using a Bayesian network trained with a given font @cite_23 .
@cite_15 @cite_37 @cite_23
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In this paper, we investigate the Chinese calligraphy synthesis problem: synthesizing Chinese calligraphy images with specified style from standard font(eg. Hei font) images (Fig. 1(a)). Recent works mostly follow the stroke extraction and assemble pipeline which is complex in the process and limited by the effect of stroke extraction. In this work we treat the calligraphy synthesis problem as an image-to-image translation problem and propose a deep neural network based model which can generate calligraphy images from standard font images directly. Besides, we also construct a large scale benchmark that contains various styles for Chinese calligraphy synthesis. We evaluate our method as well as some baseline methods on the proposed dataset, and the experimental results demonstrate the effectiveness of our proposed model. The existing method of contour-based font description is difficult to meet the personalized need for various style font generations because of the large size of Chinese character set. In this paper, we propose a novel glyph description method which treats the Chinese character as a constitution of the stable part called "structure" and the mutable part called "style". The structures of all characters are clustered by an improved K-Medoids method to guide the following generation of sample set which covers all kinds of style information of the whole character set. The result of cluster procedure indicates that radicals are bottlenecks for the reduction of sample set due to the low repetition rate in all characters. To address this problem, we present the radicals as a set of stroke-to-stroke layout structures, and render them from these substructures available in the sample set. Experiment results shows that the substitution enables us to learn the style information from a small set of sample characters (less than 10 of total amount) and generate the rest with the similar writing style. Prior knowledge of Chinese calligraphy is modeled in this paper, and the hierarchical relationship of strokes and radicals is represented by a novel five layer framework. Calligraphist’s unique calligraphy skill is analyzed and his particular strokes, radicals and layout patterns provide raw element for the proposed five layers. The criteria of visual aesthetics based on Marr’s vision assumption are built for the proposed algorithm of automatic generation of Chinese character. The Bayesian statistics is introduced to characterize the character generation process as a Bayesian dynamic model, in which, parameters to translate, rotate and scale strokes, radicals are controlled by the state equation, as well as the proposed visual aesthetics is employed by the measurement equation. Experimental results show the automatically generated characters have almost the same visual acceptance compared to calligraphist’s artwork. This paper presents a novel algorithmic method for automatically generating personal handwriting styles of Chinese characters through an example-based approach. The method first splits a whole Chinese character into multiple constituent parts, such as strokes, radicals, and frequent character components. The algorithm then analyzes and learns the characteristics of character handwriting styles both defined in the Chinese national font standard and those exhibited in a person's own handwriting records. In such an analysis process, we adopt a parametric representation of character shapes and also examine the spatial relationships between multiple constituent components of a character. By imitating shapes of individual character components as well as the spatial relationships between them, the proposed method can automatically generate personalized handwritings following an example-based approach. To explore the quality of our automatic generation algorithm, we compare the computer generated results with the authentic human handwriting samples, which appear satisfying for entertainment or mobile applications as agreed by Chinese subjects in our user study.