Few-Shot Font Generation with Deep Metric Learning
arXiv:2011.02206
Abstract
Designing fonts for languages with a large number of characters, such as Japanese and Chinese, is an extremely labor-intensive and time-consuming task. In this study, we addressed the problem of automatically generating Japanese typographic fonts from only a few font samples, where the synthesized glyphs are expected to have coherent characteristics, such as skeletons, contours, and serifs. Existing methods often fail to generate fine glyph images when the number of style reference glyphs is extremely limited. Herein, we proposed a simple but powerful framework for extracting better style features. This framework introduces deep metric learning to style encoders. We performed experiments using black-and-white and shape-distinctive font datasets and demonstrated the effectiveness of the proposed framework.
arXiv commentAccepted to ICPR 2020
References in corpus (5)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
- Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning
- From A to Z: Supervised Transfer of Style and Content Using Deep Neural Network Generators
- Few-shot Compositional Font Generation with Dual Memory