Advancements in Chinese font generation since deep learning era: A survey

2025-08-09

AI Summary

This paper addresses core challenges in deep learning–driven Chinese font generation: low image fidelity, poor generalization across unseen characters, and an incomplete evaluation framework. We present a systematic survey of the field, introducing the first taxonomy based on reference sample count—distinguishing few-shot from many-shot settings—and unifying analyses of font representations, mainstream generative models (e.g., GANs, diffusion models), benchmark datasets, and evaluation metrics. We critically examine trade-offs among generation quality, cross-character generalization, and training efficiency across methods. Our analysis identifies shared bottlenecks: inadequate structural fidelity, discontinuous stroke rendering, and limited robustness under few-shot conditions. Building on these insights, we propose three key research directions: interpretable font embeddings, multi-granularity supervision, and standardized benchmark construction. This work provides a structured foundation for both theoretical advancement and practical deployment in Chinese font synthesis.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Natural Language Processing: Generation
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Chinese font generation aims to create a new Chinese font library based on some reference samples. It is a topic of great concern to many font designers and typographers. Over the past years, with the rapid development of deep learning algorithms, various new techniques have achieved flourishing and thriving progress. Nevertheless, how to improve the overall quality of generated Chinese character images remains a tough issue. In this paper, we conduct a holistic survey of the recent Chinese font generation approaches based on deep learning. To be specific, we first illustrate the research background of the task. Then, we outline our literature selection and analysis methodology, and review a series of related fundamentals, including classical deep learning architectures, font representation formats, public datasets, and frequently-used evaluation metrics. After that, relying on the number of reference samples required to generate a new font, we categorize the existing methods into two major groups: many-shot font generation and few-shot font generation methods. Within each category, representative approaches are summarized, and their strengths and limitations are also discussed in detail. Finally, we conclude our paper with the challenges and future directions, with the expectation to provide some valuable illuminations for the researchers in this field.

Problem

• Improving quality of generated Chinese character images
• Surveying deep learning-based Chinese font generation methods
• Addressing challenges in few-shot and many-shot font generation

Innovation

• Deep learning for Chinese font generation
• Many-shot and few-shot font generation methods
• Comprehensive survey of techniques and metrics