Chinese Typeface Transformation with Hierarchical Adversarial Network

11/17/2017
by   Jie Chang, et al.
0

In this paper, we explore automated typeface generation through image style transfer which has shown great promise in natural image generation. Existing style transfer methods for natural images generally assume that the source and target images share similar high-frequency features. However, this assumption is no longer true in typeface transformation. Inspired by the recent advancement in Generative Adversarial Networks (GANs), we propose a Hierarchical Adversarial Network (HAN) for typeface transformation. The proposed HAN consists of two sub-networks: a transfer network and a hierarchical adversarial discriminator. The transfer network maps characters from one typeface to another. A unique characteristic of typefaces is that the same radicals may have quite different appearances in different characters even under the same typeface. Hence, a stage-decoder is employed by the transfer network to leverage multiple feature layers, aiming to capture both the global and local features. The hierarchical adversarial discriminator implicitly measures data discrepancy between the generated domain and the target domain. To leverage the complementary discriminating capability of different feature layers, a hierarchical structure is proposed for the discriminator. We have experimentally demonstrated that HAN is an effective framework for typeface transfer and characters restoration.

READ FULL TEXT

page 3

page 6

research
08/17/2021

DRB-GAN: A Dynamic ResBlock Generative Adversarial Network for Artistic Style Transfer

The paper proposes a Dynamic ResBlock Generative Adversarial Network (DR...
research
01/11/2021

Cycle Generative Adversarial Networks Algorithm With Style Transfer For Image Generation

The biggest challenge faced by a Machine Learning Engineer is the lack o...
research
05/08/2022

End-to-End Rubbing Restoration Using Generative Adversarial Networks

Rubbing restorations are significant for preserving world cultural histo...
research
01/13/2020

Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild

In this work we propose to improve text recognition from a new perspecti...
research
05/09/2022

So Different Yet So Alike! Constrained Unsupervised Text Style Transfer

Automatic transfer of text between domains has become popular in recent ...
research
03/16/2023

Generative Adversarial Network for Personalized Art Therapy in Melanoma Disease Management

Melanoma is the most lethal type of skin cancer. Patients are vulnerable...
research
05/21/2018

Anime Style Space Exploration Using Metric Learning and Generative Adversarial Networks

Deep learning-based style transfer between images has recently become a ...

Please sign up or login with your details

Forgot password? Click here to reset