DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learning

Abstract

nnnnnnn features of fonts to synthesize vector glyphs. Second, we provide a new generative paradigm to handle unstructured data (e.g., vector glyphs) by randomly sampling plausible synthesis results to get the optimal one which is further refined under the guidance of generated structured data (e.g., glyph images). Finally, qualitative and quantitative experiments conducted on a publicly-available dataset demonstrate that our method obtains highquality synthesis results in the applications of vector font generation and interpolation, significantly outperforming the state of the art.