EHW-Font: A handwriting enhancement approach mimicking human writing processes

Abstract

Balancing personalized style mimicry and legibility in handwritten font generation is particularly challenging for complex, multi-stroke characters like Chinese. Most existing approaches rely on a single modality – either pixel-based or sequence-based modeling – and employ random style reference selection during training, which often undermines both readability and stylistic consistency. In this paper, we introduce EHW-Font, a novel dual-modal framework that refines handwritten font generation by replicating the user’s writing style and process. Our approach fully exploits component-level, fine-grained style information from content and style characters. It employs a dual-modal fusion strategy to adaptively integrate the global visual features from handwritten stroke images with the dynamic process captured by stroke sequences. To mitigate style redundancy, we propose a quantization strategy that represents the style feature vector as the Cartesian product of one-dimensional variable sets, compressing redundant features while preserving essential stylistic details. Experiments show that our approach exhibits the best performance in qualitative, quantitative, and user studies. Moreover, our method is an equally effective means of data augmentation.