DML-Font: Multilingual Font Generation based on Diffusion Model

Automated generation of high-fidelity fonts for structurally complex scripts such as Korean and Chinese characters face significant challenges due to combinatorial glyph variations and nuanced stylistic details.While generative models like GANs and VAEs have advanced digital typography, they suffer from training instability, stroke distortions, and limited stylistic diversity.We introduce DML-Font, a diffusion-based framework that generates visually precise multilingual fonts (Korean, Chinese, English, numerals, including symbols) with unprecedented personalization.Our method employs phonetic-aware text encoding to decompose characters into linguistic components (Hangul jamo, Chinese strokes), ensuring structural integrity.Multi-reference style conditioning captures aesthetic nuances using diverse exemplars, while an iterative ResNet-based correction mechanism refines stroke alignment.Crucially, DML-Font leverages both standardized fonts and user handwriting samples to replicate personal writing styles.Quantitative evaluations demonstrate superiority printed fonts achieve SSIM 0.926 (Korean) and 0.920 (Chinese); handwritten fonts reach SSIM 0.861 (Korean) and 0.814 (Chinese), exceeding benchmarks by 6-15% across scripts.Concurrently, LPIPS (↓0.033 printed, ↓0.081 handwritten) and FID (↓12.3 printed, ↓21.2 Korean handwritten) confirm enhanced perceptual quality.The model generates complete TrueType Font sets with stylistic coherence across alphabets, numerals, and symbols.It also faithfully mimics user handwriting from minimal samples, bridging AI-driven design with cultural preservation.This work establishes a new paradigm for multilingual typography with applications in digital publishing, accessible communication, and heritage archiving.