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Arbitrary Font Generation by Encoder Learning of Disentangled Features.

Jeong-Sik Lee1, Rock-Hyun Baek2, Hyun-Chul Choi1

  • 1ICVSLab., Department of Electronic Engineering, Yeungnam University, 280 Daehak-ro, Gyeongsan 38541, Gyeongbuk, Korea.

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Summary

This study introduces a novel automatic font generation method that effectively disentangles text content and font style. The approach enables the creation of diverse, high-quality fonts, significantly improving upon existing techniques for multiple languages.

Keywords:
arbitrary font generationconsistency lossfeature disentanglementhallucinated inputstacked input

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Area of Science:

  • Computer Graphics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Font design is time-consuming and resource-intensive, especially for languages with complex character combinations.
  • Existing automatic font generation methods, often Generative Adversarial Network (GAN)-based, struggle with generating arbitrary fonts and effective content-style disentanglement.
  • Previous methods fail to sufficiently separate text content from font style, limiting their ability to generate novel font designs.

Purpose of the Study:

  • To propose a new automatic font generation method that overcomes the limitations of previous approaches in disentangling content and style.
  • To enable the generation of arbitrary, high-quality fonts from a limited set of reference images.
  • To improve the efficiency and reduce the labor involved in font design.

Main Methods:

  • Utilizes stacked inputs: images with identical text but varying styles (content input) and images with identical styles but varying text (style input).
  • Introduces novel consistency losses to enforce uniform encoded features across combined inputs, ensuring effective disentanglement.
  • Employs separate encoders for content and style to achieve robust feature extraction.

Main Results:

  • Demonstrates successful extraction of consistent text content and font style features, proving the efficacy of the separated encoders.
  • Generates unseen font designs from a small number of reference images with superior quality compared to prior methods.
  • Achieves quantitative improvements, including a 17.84 lower Fréchet Inception Distance (FID) for unseen fonts across Korean, Chinese, and English characters.

Conclusions:

  • The proposed method effectively solves the content-style disentanglement problem in automatic font generation.
  • It offers a significant advancement in generating diverse and high-quality arbitrary fonts for various languages.
  • The approach provides a more efficient and effective solution for font design compared to existing techniques.