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.
Suveeranont and Igarashi addressed a generation of alphabetical characters for typographic fonts @cite_8 . They generated characters by blending predefined characters from miscellaneous complete fonts. This method is based on a vector format, in contrast with the proposed method, which accepts an image format. Campbell and Kautz learned a manifold of standard fonts of alphabetical characters @cite_17 . Locations on the manifold represent a new font. Feng used a wavelet transform to blend two fonts @cite_2 . Tenenbaum and Freeman used complete fonts as references to form characters from the generation results @cite_28 .
@cite_28 @cite_2 @cite_17 @cite_8
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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. 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. Building a complete personalized Chinese font library for an ordinary person is a tough task due to the existence of huge amounts of characters with complicated structures. Yet, existing automatic font generation methods still have many drawbacks. To address the problem, this paper proposes an end-to-end learning system, DCFont, to automatically generate the whole GB2312 font library that consists of 6763 Chinese characters from a small number (e.g., 775) of characters written by the user. Our system has two major advantages. On the one hand, the system works in an end-to-end manner, which means that human interventions during offline training and online generating periods are not required. On the other hand, a novel deep neural network architecture is designed to solve the font feature reconstruction and handwriting synthesis problems through adversarial training, which requires fewer input data but obtains more realistic and high-quality synthesis results compared to other deep learning based approaches. Experimental results verify the superiority of our method against the state of the art. Based on the cubic B-spline curve, new Chinese fonts are generated by wavelet transforms in this paper. The outlines of Chinese fonts are first transformed into B-spline curves. Then, using wavelet transforms, the control points of each curve are decomposed into hierarchies containing the detailed features of the Chinese fonts. Using the synthesis procedure of wavelet transforms, new fonts can be generated by modifying details at selected hierarchies.