Reading the Unreadable: Artificial Intelligence for Text Interpretation in Degraded Manuscripts

“Manuscripts do not burn”, goes the famous maxim pronounced by the Devil-Woland in M.A. Bulgakov’s masterpiece The Master and Margarita. Alluding to the eternal value of the Word, this formula also appears to fit the material reality of manuscripts as concrete historical documents. This study addresses the methodological and epistemological implications of employing artificial intelligence in the study of severely damaged manuscripts, proposing a paradigm shift from traditional Handwritten Text Recognition (HTR) systems that fail under regimes of severe material degradation. Three reference computer vision architectures are compared on the multispectral MADAM dataset: object detection (Faster R-CNN), semantic segmentation (U-Net), and instance segmentation (Mask R-CNN), evaluated on incremental training subsets ranging from 6 to 84 patches. Semantic segmentation outperforms both object detection and instance segmentation across all metrics, achieving stable performance from 24 annotated patches; conversely, Faster R-CNN and Mask R-CNN architectures require larger datasets to avoid divergence during training (Carcagnì et al. 2026). However, U-Net encounters a structural limitation in case of thin or nearly transparent strokes where residual ink is insufficient to support segmentation; under such conditions, textual completion requires the expert’s judgment. This work, therefore, justifies a human-in-the-loop (HITL) approach in which algorithmic analytical capabilities and philological, linguistic, and semiotic expertise operate in a complementary manner throughout the entire recognition pipeline. A new hermeneutic paradigm, where the acronym AI alludes to a necessary collaboration between Artificial Intelligence and Artisan Intelligence – for the safeguarding of Artistic Intelligence of mankind.