Calligraphy Font Generation via Explicitly Modeling Location-Aware Glyph Component Deformations

IEEE TRANSACTIONS ON MULTIMEDIA(2024)

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Abstract
Automatic font generation is a challenging and time-consuming task, particularly in languages that consist of large amounts of characters with complicated structures. Typical component-wise font generation methods decompose the source character into components and search for them from the reference glyph set as candidate components. These candidate components are then utilized to learn the local styles of the target glyph. However, these methods overlook that the same component at different locations may have different profiles. When the candidate components locate differently from their corresponding components in the target glyph, the style of a generated glyph will look inconsistent. It is observed that for arbitrary components at two specific locations, the deformation patterns are similar. Driven by this, we present a location-aware component-deformable font generation method. Specifically, we search for candidate components and their corresponding deformative component pairs from the reference glyph set. Each deformative component pair can accurately depict how to deform the candidate component to the desired profile in the target glyph. Hence, we introduce a location-dependent deformation module to perform component warping. In this way, we significantly improve the component deformation ability. Lastly, we integrate deformed components into target glyphs while enforcing their styles to be consistent with the reference ones. Extensive experiments demonstrate that our method produces target-font consistent glyphs and outperforms the state-of-the-art on both seen and unseen fonts.
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Key words
Generative adversarial networks,image processing,image synthesis,image transformation
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