Pre-Editorial Normalization for Automatically Transcribed Medieval Manuscripts in Old French and Latin

• URL: http://arxiv.org/abs/2602.13905v1
• Date: Sat, 14 Feb 2026 21:55:30 GMT
• Title: Pre-Editorial Normalization for Automatically Transcribed Medieval Manuscripts in Old French and Latin
• Authors: Thibault Clérice, Rachel Bawden, Anthony Glaise, Ariane Pinche, David Smith,
• Abstract summary: We introduce the task of Pre-Editorial Normalization (PEN), which consists in normalizing graphemic ATR output according to editorial conventions.<n>We present a new dataset derived from the CoMMA corpus and aligned with digitized Old French and Latin editions using passim.<n>We benchmark this resource using ByT5-based sequence-to-sequence models on normalization and pre-annotation tasks.
• Score: 9.171446868270468
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in Automatic Text Recognition (ATR) have improved access to historical archives, yet a methodological divide persists between palaeographic transcriptions and normalized digital editions. While ATR models trained on more palaeographically-oriented datasets such as CATMuS have shown greater generalizability, their raw outputs remain poorly compatible with most readers and downstream NLP tools, thus creating a usability gap. On the other hand, ATR models trained to produce normalized outputs have been shown to struggle to adapt to new domains and tend to over-normalize and hallucinate. We introduce the task of Pre-Editorial Normalization (PEN), which consists in normalizing graphemic ATR output according to editorial conventions, which has the advantage of keeping an intermediate step with palaeographic fidelity while providing a normalized version for practical usability. We present a new dataset derived from the CoMMA corpus and aligned with digitized Old French and Latin editions using passim. We also produce a manually corrected gold-standard evaluation set. We benchmark this resource using ByT5-based sequence-to-sequence models on normalization and pre-annotation tasks. Our contributions include the formal definition of PEN, a 4.66M-sample silver training corpus, a 1.8k-sample gold evaluation set, and a normalization model achieving a 6.7% CER, substantially outperforming previous models for this task.

Related papers

• Discourse Features Enhance Detection of Document-Level Machine-Generated Content [53.41994768824785]
  Machine-generated content poses challenges such as academic plagiarism and the spread of misinformation.<n>Existing MGC detectors often focus solely on surface-level information, overlooking implicit and structural features.<n>We introduce novel methodologies and datasets to overcome these challenges.
  arXiv  Detail & Related papers  (2024-12-17T08:47:41Z)
• Neural Text Normalization for Luxembourgish using Real-Life Variation Data [21.370964546752294]
  We propose the first sequence-to-sequence normalization models using the ByT5 and mT5 architectures.<n>We show that our sequence model using real-life variation data is an effective approach for tailor-made normalization in Luxembourgish.
  arXiv  Detail & Related papers  (2024-12-12T15:50:55Z)
• Reference-Based Post-OCR Processing with LLM for Precise Diacritic Text in Historical Document Recognition [1.6941039309214678]
  We propose a method utilizing available content-focused ebooks as a reference base to correct imperfect OCR-generated text.<n>This technique generates high-precision pseudo-page-to-page labels for diacritic languages.<n>The pipeline eliminates various types of noise from aged documents and addresses issues such as missing characters, words, and disordered sequences.
  arXiv  Detail & Related papers  (2024-10-17T08:05:02Z)
• Is text normalization relevant for classifying medieval charters? [0.0]
  This study examines the impact of historical text normalization on the classification of medieval charters.<n>Our results indicate that the given normalization minimally improves locating tasks but reduces accuracy for dating.<n>Results suggest a selective approach to historical text normalization, emphasizing the significance of preserving some textual characteristics.
  arXiv  Detail & Related papers  (2024-08-29T11:19:57Z)
• RDBE: Reasoning Distillation-Based Evaluation Enhances Automatic Essay Scoring [0.0]
  Reasoning Distillation-Based Evaluation (RDBE) integrates interpretability to elucidate the rationale behind model scores. Our experimental results demonstrate the efficacy of RDBE across all scoring rubrics considered in the dataset.
  arXiv  Detail & Related papers  (2024-07-03T05:49:01Z)
• How to Choose Pretrained Handwriting Recognition Models for Single Writer Fine-Tuning [23.274139396706264]
  Recent advancements in Deep Learning-based Handwritten Text Recognition (HTR) have led to models with remarkable performance on modern and historical manuscripts. Those models struggle to obtain the same performance when applied to manuscripts with peculiar characteristics, such as language, paper support, ink, and author handwriting. In this paper, we take into account large, real benchmark datasets and synthetic ones obtained with a styled Handwritten Text Generation model. We give a quantitative indication of the most relevant characteristics of such data for obtaining an HTR model able to effectively transcribe manuscripts in small collections with as little as five real fine-tuning lines
  arXiv  Detail & Related papers  (2023-05-04T07:00:28Z)
• Text Embeddings by Weakly-Supervised Contrastive Pre-training [98.31785569325402]
  E5 is a family of state-of-the-art text embeddings that transfer well to a wide range of tasks. E5 can be readily used as a general-purpose embedding model for any tasks requiring a single-vector representation of texts.
  arXiv  Detail & Related papers  (2022-12-07T09:25:54Z)
• DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks [88.62288327934499]
  We propose a novel augmentation method with language models trained on the linearized labeled sentences. Our method is applicable to both supervised and semi-supervised settings.
  arXiv  Detail & Related papers  (2020-11-03T07:49:15Z)
• POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training [93.79766670391618]
  We present POINTER, a novel insertion-based approach for hard-constrained text generation. The proposed method operates by progressively inserting new tokens between existing tokens in a parallel manner. The resulting coarse-to-fine hierarchy makes the generation process intuitive and interpretable.
  arXiv  Detail & Related papers  (2020-05-01T18:11:54Z)
• Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning [61.32992639292889]
  Fine-tuning of pre-trained transformer models has become the standard approach for solving common NLP tasks. We introduce a new scoring method that casts a plausibility ranking task in a full-text format. We show that our method provides a much more stable training phase across random restarts.
  arXiv  Detail & Related papers  (2020-04-29T10:54:40Z)
• Document Ranking with a Pretrained Sequence-to-Sequence Model [56.44269917346376]
  We show how a sequence-to-sequence model can be trained to generate relevance labels as "target words" Our approach significantly outperforms an encoder-only model in a data-poor regime.
  arXiv  Detail & Related papers  (2020-03-14T22:29:50Z)
• Towards Making the Most of Context in Neural Machine Translation [112.9845226123306]
  We argue that previous research did not make a clear use of the global context. We propose a new document-level NMT framework that deliberately models the local context of each sentence.
  arXiv  Detail & Related papers  (2020-02-19T03:30:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.