
Pierfrancesco Giambullari’s Del sito, forma, & misure, dello Inferno di Dante (Florence: Neri Dortelata, 1544) is a representative case of what we call primary-source typographic idiosyncrasy: a single pseudonymous 16th-century press with an experimental dense stress-accent orthography, long-s ß ligatures, and scribal tilde abbreviations, none of which appears in any modern OCR training distribution. On this text we report a character error rate of 2.48%, measured on a 7-page hand-corrected leakage-free held-out set (7,660 characters, 190 edits), using a LoRA fine-tune of LightOnOCR-2-1B on 16 training pages as the OCR engine. Training took ∼ 15 minutes on a single H100; total compute cost for all experiments, including full-book inference on 151 pages, was US$6.96. For context, a zero-shot Claude Sonnet 4.5 baseline reaches 8.12% CER on the same gold standard, and the author’s first-pass Transkribus PyLaia custom HTR model (20 manually annotated training pages) reaches 15.66% CER; both numbers are reported as reference points, not as a head-to-head ranking, and the Transkribus baseline would likely improve with more effort. The headline of this note is not that one OCR stack “wins”, but that off-the-shelf VLM-OCR with a small LoRA fine-tune, produced by a single person on an iPhone-scanned book in one day, reaches a CER suitable for critical-edition workflows on exactly the kind of idiosyncratic primary-source typography where zero-shot frontier models still degrade. A complementary finding concerns stress-accent coverage: the fine-tuned LightOn model preserves +26% more accented characters than the Transkribus baseline corpus-wide, a difference that matters for a Dortelata edition where stress accents are lexically load-bearing. We release this note ahead of the forthcoming critical edition to document the pipeline and the accent-coverage finding.
OCR, Giambullari, Renaissance, historical document recognition, early modern Italian, LightOnOCR, VLM, digital humanities, Dortelata, LoRA
OCR, Giambullari, Renaissance, historical document recognition, early modern Italian, LightOnOCR, VLM, digital humanities, Dortelata, LoRA
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