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The study evaluates how nineteenth‑century scholarship—specifically Pressutti’s regesta and partial editions—can aid handwritten text recognition (HTR) of Pope Honorius III’s registers beyond the limited modern expert transcriptions available for regnal years 1‑2. By aligning legacy layers with modern source lines, the authors demonstrate that projection onto curial spelling with replay reduces word‑error rate by 1.50 points, that a legacy lexicon covers 82.2 % of out‑of‑vocabulary words and flags errors with 66.7 % precision, and that a distilled medieval NER model achieves an F1 of 82.1 % on a validated benchmark. Additionally, Pressutti’s regesta and related topological data recover 73.9 % of entry starts and suggest a character‑error rate of 7.80 % beyond folio 203, indicating that antiquarian scholarship can steer, check, and segment HTR rather than replace modern transcription.
The paper investigates how nineteenth‑century scholarship—specifically Pressutti’s regesta and partial editions—can aid handwritten text recognition (HTR) for the registers of Pope Honorius III, which lack modern expert transcriptions beyond regnal years 1‑2. By comparing legacy layers to audited transcriptions, the authors identify a three‑tier division of labor: at the orthographic tier, projecting legacy editions onto curial spelling with replay reduces word‑error rate by 1.5 points; at the lexical tier, a legacy lexicon covers 82.2 % of out‑of‑vocabulary words and improves NER performance; and at the topological tier, regesta data help locate entry starts with 73.9 % coverage and estimate a character‑error rate of 7.80 % beyond folio 203. The study shows that while antiquarian scholarship cannot replace modern transcription, it can effectively steer, verify, and segment HTR across untranscribed registers.
RefLAM is a pipeline that converts manuscript page images and clean transcriptions into validated, line-level ground truth for Arabic handwritten text recognition. It combines a deep‑learning page‑segmentation model, a multimodal large language model for structured OCR, and a diacritic‑agnostic fuzzy alignment engine that assigns a confidence score to each line, with a provable correctness guarantee for perfect scores. Using RefLAM, the authors achieved a 75× speedup over manual annotation and released AraMS‑28k, a dataset of 14 historical Arabic manuscripts with detailed annotations.
arXiv:2607. 08143v1 Announce Type: cross Abstract: We present the results of HIPE-OCRepair-2026, an ICDAR competition on LLM-assisted OCR post-correction of historical documents.
arXiv:2608. 19385v1 Announce Type: new Abstract: Historical Arabic manuscript transcription is not only a recognition problem.
arXiv:2607. 03836v1 Announce Type: cross Abstract: Despite remarkable progress in machine translation, Vision Language Models (VLMs) struggle on historical manuscripts, a domain that stresses core Natural Language Processing (NLP) capabilities: low-resource transliteration, archaic vocabulary, and noisy input signals.