Generative Text Modelling and Language-Guided Visual Attention for Deciphering Palimpsests
Recovering obscured text in palimpsests, i.e. the manuscripts where the original writing (under-text) was erased and overwritten for reuse, is vital for preserving lost information. Due to the nature of the material, such a task can be highly challenging and time-consuming. This paper presents an approach towards automated palimpsest handwritten text modeling for under-text recovery. We investigate generative models, including Generative Adversarial Networks (GANs) and diffusion models, and design a custom denoising diffusion model that learns structured noise patterns to reconstruct the under-text. The recovered text is subsequently processed by a fine-tuned, attention-based handwritten ...
| Authors | Robin Hollifeldt, Tianru Zhang, Jessica Ström et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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