Handwritten Text Generation with Diffusion Models: Beyond Visual Quality
Abstract
Handwritten Text Generation (HTG) has emerged as a promising remedy for data scarcity that limits the training of Deep Learning (DL) models for Document Image Analysis and Recognition (DIAR) tasks. Current HTG pipelines are mostly based on adversarial training, which can face several issues, such as mode collapse, limiting variability, and in turn, reducing the practical usefulness of the generated data. Moreover, existing evaluation protocols prioritize visual realism rather than usefulness, obscuring the link between generative quality and downstream task performance. These limitations raise several questions related to which HTG methods truly benefit DIAR tasks, which real datasets should serve as a foundation for synthetic data generation, how Diffusion Models can be adapted for controllable and robust HTG, and whether current evaluation protocols capture downstream utility. Given the success of text-to-image Diffusion Models in generating realistic images from text prompts, their adaptation for handwriting generation offers the potential to produce high-quality, style-aware handwritten data that can directly enhance Handwritten Text Recognition (HTR). The overarching goal of this thesis is to bridge the gap between generation and task-aligned evaluation by showing how controllable diffusion-based HTG, guided by systematic data analysis and evaluated through practical utility, can address these challenges. The contributions of this thesis are organized along three…
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