Learning to Generate Stylized Handwritten Text via a Unified Representation of Style, Content, and Noise

Handwritten Text Generation (HTG) seeks to synthesize realistic and personalized handwriting by modeling stylistic and structural traits. While recent diffusion-based approaches have advanced generation fidelity, they typically rely on auxiliary style or content encoders with handcrafted objectives, leading to complex training pipelines and limited interaction across factors. In this work, we present InkSpire, a diffusion transformer based model that unifies style, content, and noise within a shared latent space. By eliminating explicit encoders, InkSpire streamlines optimization while enabling richer feature interaction and stronger in-context generation. To further enhance flexibility, we introduce a multi-line masked infilling strategy that allows training directly on raw text-line images, together with a revised positional encoding that supports arbitrary-length multi-line synthesis and fine-grained character editing. Moreover, InkSpire is trained on a bilingual Chinese–English corpus, enabling a single model to handle both Chinese and English handwriting generation with high fidelity and stylistic diversity, thereby overcoming the need for language-specific systems. Extensive experiments on IAM and ICDAR2013 demonstrate that InkSpire achieves superior structural accuracy and stylistic diversity compared to prior state-of-the-art methods.