Coconditional Autoencoding Adversarial Networks for Chinese Font Feature Learning

TL;DR

This paper introduces CocoAAN, a novel adversarial framework for Chinese font learning that jointly encodes style and content features and generates realistic glyphs without complex segmentation, demonstrating strong generalization.

Contribution

The paper presents a new unsupervised approach for Chinese font generation that combines co-conditional autoencoding with adversarial training, avoiding complex segmentation.

Findings

Successfully generates realistic Chinese glyphs

Generalizes well to unseen fonts and characters

Avoids complex stroke or component segmentation

Abstract

In this work, we propose a novel framework named Coconditional Autoencoding Adversarial Networks (CocoAAN) for Chinese font learning, which jointly learns a generation network and two encoding networks of different feature domains using an adversarial process. The encoding networks map the glyph images into style and content features respectively via the pairwise substitution optimization strategy, and the generation network maps these two kinds of features to glyph samples. Together with a discriminative network conditioned on the extracted features, our framework succeeds in producing realistic-looking Chinese glyph images flexibly. Unlike previous models relying on the complex segmentation of Chinese components or strokes, our model can "parse" structures in an unsupervised way, through which the content feature representation of each character is captured. Experiments demonstrate our framework has a powerful generalization capacity to other unseen fonts and characters.