FontUse: A Data-Centric Approach to Style- and Use-Case-Conditioned In-Image Typography

ArXi:2603.06038v1 Announce Type: new Recent text-to-image models can generate high-quality images from natural-language prompts, yet controlling typography remains challenging: requested typographic appearance is often ignored or only weakly followed. We address this limitation with a data-centric approach that trains image generation models using targeted supervision derived from a structured annotation pipeline specialized for typography.