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

Thibault Clérice, Rachel Bawden, Anthony Glaise, Ariane Pinche, Smith David

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Thibault Clérice, Rachel Bawden, Anthony Glaise, Ariane Pinche, Smith David. Pre-Editorial Normalization for Automatically Transcribed Medieval Manuscripts in Old French and Latin. 2026. hal-05514246

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

Thibault Clérice,<fnref n="1" /> Rachel Bawden,<fnref n="1" /> Anthony Glaise,²
Ariane Pinche,³ David Smith⁴

¹Inria, Paris, France ²CNRS (UMR 7323), Tours, France,
³CNRS (UMR 5648), Lyon, France, ⁴Northeastern University
{firstname.lastname}@{inria,cnrs}.fr, dasmith@ccs.neu.edu

## 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.

**Keywords:** Latin, Old French, Normalization, Text Recognition, Medieval manuscripts

## 1. Introduction

The progress of Automatic Text Recognition (ATR) has facilitated access to historical archives (Pinche and Stokes, 2024). However, there exists a methodological divide in how to transcribe manuscripts on a spectrum ranging between two extremes: the graphetic approach and a normalized approach (Figure 1). The graphetic approach, defined by Robinson and Solopova (1993), treats transcription as a graphical representation of the manuscript's text, which captures each character variant, and maintains original punctuation and abbreviation without expansion. Normalized transcription conflates transcribing with textual editing (Pierazzo, 2011), thus adhering more to users expectations by "[...] [making] the manuscript text accessible to the modern reader by expanding abbreviations, separating words, inserting diacritical marks, and adding modern punctuation" (Duval, 2012, p.330). However, this approach, combining two tasks at the same time, a graphical interpretation and a linguistic interpretation, has shown to poorly resolve abbreviations and to hallucinate content (Aguilar, 2024; Torres Aguilar and Jolivet, 2023), without providing an ability for the reader to distinguish normalization errors from graphical interpretation errors.

It can be useful to follow an approach between the two extremes, whereby character variants are merged but where abbreviations and other typi-

<figure><img src="image_2.png" /></figure>

Graphetic | imodatis uenïs uſib3 natãlis calo2

Graphemic | imodatis uenïs usib; natãlis calor

Normalized | inmoderatis veneris usibus naturalis calor

<caption>Figure 1: The outputs of different transcription approaches for the same line of text. Manuscript: BSB CLM 13027. The task defined in this paper consists in producing normalized transcriptions from graphemic outputs.</caption>

cal manuscripts phenomena are preserved: this is known as the graphemic approach. This enables the transcriptions to be treated as their own epistemic objects, which is an advantage in itself. It also results in transcriptions that result in models, such as those trained on the CATMuS dataset (Clérice et al., 2024), that better generalize to new domains and languages (Coll Ardanuy et al., 2025).

However, graphemic transcriptions create a “usability gap” where the most accurate transcriptions of the material reality of the text are the least compatible with most available tools for computational analysis and expectations from the humanities (Kiessling, 2025).<fnref n="1" /> It can therefore be use-

<footnote n="1">¹Tools such as lemmatizers, part-of-speech (PoS) taggers, and Named Entity Recognition (NER) systems</footnote>
ful to automatically produce normalized versions
of these transcriptions. It holds an advantage over
all-in-one normalized ATR approaches, as it pro-
vides an intermediate layer that makes it possible
to distinguish ATR error (such as <rn>/<m>) from nor-
malization errors (‘&’ for ‘est et’ or ‘esset’).

In this paper, we introduce the task of auto-
matically producing normalized transcriptions from
graphemic ATR transcriptions, which we name pre-
editorial normalization (PEN). The task definition
and rationale is followed by the introduction of a
novel dataset, based on the ATR silver corpus
CoMMA (Clérice et al., 2025) and digitized and
born-digital editions of Old French and Latin texts.
Using *passim* (Smith et al., 2015), we automati-
cally aligned both corpora, of which we then man-
ually corrected some of the alignments to produce
a gold-standard test set. We benchmark the sil-
ver dataset using models trained on a sequence-
to-sequence transformer, ByT5 (Xue et al., 2022),
across two tasks: (i) a normalization task, for
which we consider our gold test set as the goal,
and (ii) a pre-annotation task for lemmatization and
PoS-tagging.

## 2. Related Work

### 2.1. Automatic Text Recognition with Normalized Output

ATR research has generally paid limited attention
to the distinction between imitative transcription of
historical sources and their subsequent normaliza-
tion. Historical text recognition models are often
evaluated on resources such as the IAMHistDB
medieval datasets, the Perzival (Fischer et al.,
2009), and the Saint Gall Database (Fischer et al.,
2011), despite the fact that these corpora rely on
different transcription paradigms. Camps et al.
(2022) benchmarked both approaches on genre-
constrained documents but without providing de-
tailed qualitative analyses of the results.

By contrast, several studies within the Digital Hu-
manities community have explicitly addressed the
risks associated with over-normalization. In partic-
ular, two recent works have examined this issue as
a central research question. Bottaioli et al. (2024)
show that models trained to jointly transcribe and
normalize middle names in digitized birth certifi-
cates may hallucinate names for individuals who
did not possess one. Similarly, in the context of
medieval manuscripts and, more broadly, in lan-
guages with highly variable orthography, Torterolo-
Orta et al. (2025) demonstrate that increased ac-

cessibility through normalization entails a latent
risk of systematically erasing historically and lin-
guistically meaningful variation.

Conversely, qualitative error analyses con-
ducted in several studies by S. T. Aguilar highlight
persistent limitations of models trained on normal-
ized outputs, particularly with respect to abbrevi-
ation resolution. Torres Aguilar and Jolivet (2023)
show that morphosyntactic errors constitute one of
the most frequent sources of abbreviation-related
errors. Moreover, Aguilar (2024) report several
critical failures, including difficulties in handling
line-end hyphenation, the misinterpretation of sim-
ple abbreviations (e.g., ‘īfusū’ for ‘infusum’ tran-
scribed as ‘in futurum’) even in relatively straight-
forward contexts, and errors in local semantic dis-
ambiguation, such as interpreting ‘dia’ as ‘dia-
conus’ ‘deacon’ instead of ‘diabolus’ ‘devil’.

### 2.2. Historical Text Normalization

Datasets for historical text normalization remain
scarce, particularly for Latin. To our knowledge,
the only comprehensive and readily available non-
synthetic dataset for Latin historical normalization
is the work of Schonhardt (2025), which is limited
to a single text attested in six manuscripts. This
restricted scope limits its suitability for studying ab-
breviation expansion in a broader diachronic and
bilingual context. A small number of additional
datasets have been produced or identified in the
work of Camps et al. (2022), some of which include
Old French. However, their coverage remains lim-
ited in terms of genre and textual diversity.

Related work by Büttner (2025) addresses the
normalization of Latin spelling by aligning medieval
forms with more classical orthographic standards
using a ByT5-based approach. While this study
provides a relevant example of normalization in the
Latin domain, it focuses on orthographic regular-
ization rather than pre-editorial normalization.

By contrast, normalization for vernacular lan-
guages has received greater attention, begin-
ning with late Middle French and extending into
the early modern period. Solfrini et al. (2025)
present a study on the normalization of printed
late Middle French texts from the 1530s. More
broadly, the automatic standardization of late Mid-
dle French has been explored in several works
(Rubino et al., 2024b,a). These approaches are
similar in methodology but differ in purpose from
ours, as their primary aim is to reduce endemic
linguistic variation rather than to preserve philolog-
ical and historical features. On this topic, Bawden
et al. (2022) compare normalization models for
early modern French (17th c. and later), including
datasets and comparative assessments of statisti-
cal and neural machine translation approaches.

<footnote>are typically trained on normalized, modern-style edi-
tions. When faced with the raw, non-resolved output of
a graphemic ATR model, the performance of these tools
drops significantly (Clérice and Pinche, 2025).</footnote>
Finally, digital editions may encode such information using a variety of representation frameworks, as illustrated in doctoral work (Franzini, 2019) and other projects (Nury, 2024). However, beyond the technical challenges involved in interpreting and exporting data from these platforms, identifying suitable editions remains a difficult task. To date, the only available catalogue providing filters for downloadable TEI resources (Andorfer and Franzini, 2025) indicates that, among more than 29 digital editions with accessible XML-TEI files for Latin and French texts, none preserves abbreviated forms with explicit markup.<fnref n="2" />

## 2.3. Normalization of User-Generated Content (UGC)

Another type of data associated with the lexical normalization task is user-generated content (UGC), of which social media texts are the most prevalent (Baldwin et al., 2013). UGC, like the historical data dealt with in this article, is characterised by non-standard phenomena such as acronyms, abbreviations, truncations and variations in spelling. Such texts can present challenges for natural language processing (NLP) tools, particularly those trained on standard, edited texts, which has led to a body of work in normalisation as a preprocessing step (Melero et al., 2012; Baldwin et al., 2015; van der Goot et al., 2021; Nishimwe et al., 2024a).

Initial approaches relied on rules, lexicons and language models, and it was common to decompose the task into the detection of words to be modified and their normalisation (Baranes, 2015; Ikeda et al., 2016). Later approaches opt for a translation-like approach, where the task is treated as the translation of a non-standard to standard language sequence (Aw et al., 2006; De Clercq, Orphée and Desmet, Bart and Schulz, Sarah and Lefeber, Els and Hoste, Veronique, 2013). This approach has the advantage of increased use of context with the sequence and flexibility, helpful notably for segmentation and reordering differences.

The choice of representation has been a subject of much research in the handling of UGC texts (both for normalisation and other NLP tasks). Photonic representations have been explored (Rosales Núñez et al., 2019; Mandal and Nanmaran, 2018), particularly for spelling errors. Character representations have also shown promise in terms of increasing robustness against phenomena such as spelling errors, devowelling, acronyms and abbreviations (Belinkov and Bisk, 2018; Watson et al., 2018; Domingo and Casacuberta, 2021; Scherrer and Ljubešić, 2021). Character-level representations have the advantage of better capturing similarities between similar but non-identical lexical

forms. It is an approach that has previously been shown to work well for morphologically rich languages (which are also characterised by a higher level of variation) and low-resource scenarios (in order to generalize better across forms). For this reason, another successful approach is to either collect and annotate more data or to generate synthetic data (Belinkov and Bisk, 2018; Vaibhav et al., 2019; Nishimwe et al., 2024b).

Recent work has shown that models trained on large quantities of data, and in particular large language models (LLMs) are already “more robust” in that they perform better than previously on non-standard UGC data, without being specifically trained to do so and without having to apply a specific normalization pre-processing step (Bawden and Sagot, 2023; Peters and Martins, 2025; Bawden and Sagot, 2025).<fnref n="3" /> However, this is unlikely to be the case across all languages, and in particular low-resource ones.

## 2.4. Automatic Alignment

Aligning multiple data representations has been an important method for dataset creation since the application of dynamic time warping in speech recognition (Sakoe and Chiba, 1978) or dynamic programming for aligning ATR outputs or multiple translations of the same source (Pial and Skiena, 2023; Thai et al., 2022). This paper uses methods developed for text-reuse detection, which is a broad task with many possible approaches and evaluations (Moritz et al., 2016). As described below (§4.2), the main inference task in creating our dataset is to match and align the ATR output of manuscript pages with passages in normalized editions. Unlike in other ATR evaluation tasks, these normalized editions do not have page breaks that match any particular manuscripts and the manuscripts are often miscellanies with several works. The size of the corpus makes it impractical to compare all pairs of ATR pages and passages from digital editions (Paju et al., 2022). We therefore employ passim 2.0 (Smith et al., 2023), which uses hashed character n-gram fingerprinting to filter the set of candidate passages for alignment. Once a pair of passages is selected, passim uses a character-level hidden Markov alignment model (Vogel et al., 1996). Unlike text-reuse packages that use the Smith-Waterman alignment algorithm (Smith et al., 2014; Funk and Mullen, 2018), the HMM approach is more robust to the rearrangement of passages in the ATR output due to uncertainty in page layout analysis.

<footnote n="3">³Given that they are trained on large amounts of web data, it can be debated whether the models are actually more robust or whether the non-standard forms are simply sufficiently common in the training data.</footnote>

<footnote n="2">²Sahle et al. (2020) does not provide such metadata.</footnote>
# 3. Pre-editorial Normalization

## 3.1. Task Definition

The output of graphemic ATR<fnref n="4" /> is inherently raw and reflects both manuscript-specific phenomena and transcription guidelines. As a result, it exhibits several sources of variability: spacing is inconsistent (including but not limited to unmarked line-break hyphenation), capitalization—especially in earlier centuries—may serve a decorative rather than linguistic function, numerical expressions vary across systems (ranging from Roman numerals to fractional notation in later periods), ramist letter pairs («u/v» and «i/j») are not systematically distinguished, punctuation practices frequently differ from those adopted in modern editorial standards, and abbreviations are preserved. Finally, especially in the context of raw ATR output, recognition errors are superimposed on top of the multiple sources of variation described above.

These characteristics considerably limit the direct usability of ATR outputs in standard NLP and text-processing pipelines. Moreover, they diverge substantially from the normalized representations typically expected by scholars in the humanities, thereby motivating the need for dedicated normalization and post-processing methods.

The PEN task consists in:

* normalizing letters that were considered allographs in the middle ages and that diverged over time, such as ramist letters («u»/«v») in consonantal and vocalic usage. Historical spelling variants are not normalized; for instance, «β» replacing an etymological «ph» is preserved.

* resolving abbreviations in a context-sensitive manner: expansions should reflect not only the semantic content of words, but also their morphosyntactic features and a coherent orthographic system within the text.

* addressing recognition errors in the context of raw ATR output, such as «rn»/«m» confusions.

* normalizing capitalization around punctuation and proper names according to modern editorial standards.

* preserving original punctuation.

## 3.2. Limits of the Task

Certain abbreviations, especially those referring to personal names or place names, should not be expanded when the available knowledge in context is insufficient to support a reliable interpretation. For example, the abbreviation «G. dixit» ‘G. said’ in a medical text can be reliably expanded to «Galenus dixit» based on contextual evidence. On the other hand, in a charter or accounts, there might not be any contextual elements to resolve the name of S. in «S. Parisiensis» ‘S. the Parisian’, which could be many things (Stephanus, Severinus, etc.) and the initial should therefore be kept as it is.

Severe or unprocessable ATR errors resulting in extended spans of noise should not be corrected by the model. Attempting to normalize such segments risks introducing over-normalization and, more critically, hallucinated content. Given the text-based nature of the task, only recoverable textual information should be considered.

Numerical expressions written in Roman or Arabic numerals are likewise left unchanged. This form of variation remains an open issue within the community and requires further standardization before being fully integrated into the task definition. Moreover, the distinction between ordinal and cardinal numbers is often expressed through contextual or morphological markers and may require complex morphosyntactic analysis to be resolved reliably on top of the inherent variation of Roman numeral spelling.

Sufficient contextual information is required at all levels (semantic, morphosyntactic, and spelling) to support reliable normalization decisions. However, contextual evidence may itself be conflicting. For instance, if both «molt» and «moult» occur within the same Old French text, it remains unclear how the abbreviated form «mlt» should be expanded.

In this paper, we frame the task as a single input-single output problem. In future work, the PEN task should also involve a processing layer capable of representing a distribution over possible outputs, to account for spelling variations within a single document, which can be seen in Old French («segnor» and «signor» on the same page).

# 4. Dataset Construction

## 4.1. Source Data

Our data for historical transcriptions is drawn from the CoMMA corpus, a graphemic-transcription corpus of 33,000 manuscripts (3.3B whitespace-delimited tokens). In our approach, we use the main text of each page to build full-text manuscript pages, ignoring running headers, marginal zones, and interlinear comments, thanks to the dataset's

<footnote n="4">⁴Robinson and Solopova (1993) defines graphemic as a transcription keeping original practices such as abbreviation but merging variants of same characters, such as «s» and «ß». They are implemented and more broadly defined in (Clérice et al., 2024).</footnote>
fine-grained details concerning layout classification. While manuscripts come with language metadata, we do not filter based on it, as it represents document-scale language the metadata: our alignment could find excerpts of Latin or Old French in manuscripts not annotated as such.

For the normalized texts to align with CoMMA, we use publicly available data for Latin, and a mix of closed and open data for Old French, to ensure a large enough pool of data to align with the manuscripts. Both the Latin pool and the Old French one are drawn from the same data cited by CoMMA for their ModernBert model (Clérice et al., 2025, Table 6). Some of the corpus contains highly technical data (mathematics, medical, etc.), which we expect to be the hardest to normalize in Latin, as it contain higher levels of special vocabulary and specific abbreviations.

## 4.2. Alignment

We align the ATR transcripts of the CoMMA corpus with the plain text of digital editions of Latin and Old French texts using passim 2.0 (Smith et al., 2023). This version of the package implements a character-level HMM alignment algorithm (Vogel et al., 1996), which permits rearrangement of passages between two versions of a text. This is helpful in working with ATR output, when the sequence of page regions in the transcript does not match the sequence as transcribed in a scholarly edition. In contrast to other ATR ground-truth datasets produced by forced alignment, we do not need to specify in the input which manuscripts are versions of which texts; rather, we specify to passim that each page from CoMMA should be aligned to one or more pages from the corpus of digital editions. We performed unicode decomposition normalization on both the CoMMA ATR output and the digital editions so that the common use of diacritics for, e.g., nasalization («<ɔ̃sul» for «<consul») or truncation («<ɛ» for «<cum») would align more closely to their standard forms. To improve recall, we index the collection with character 10-grams (ignoring whitespace and punctuation) that occur in fewer than 100 pages. It then aligns pages having at least five 10-grams in common and drops alignments shorter than 50 characters. We use beam search ($b = 200$) when aligning pages.

From the automatic alignment, we filter the data with at least five lines of continuous aligned content, a sufficiently large match rate (60%), and only where the alignment comes from the same source text. This resulted in 3.2M alignments. We then reprocessed them into smaller alignments, with a maximum and minimum size of 1000 and 300 bytes respectively, to get down to a total of 4,665,509 pairs.

<table><caption>Table 1: Top works by manuscript presence.</caption><thead><tr><td>Corpus</td><td>Work</td><td># Mss.</td></tr></thead><tbody><tr><td>PL</td><td>Auctor Incertus - Missale mixtum</td><td>2090</td></tr><tr><td>PL</td><td>Gratianus - Concordia discordantium canonum</td><td>2026</td></tr><tr><td>encyMe</td><td>Speculum doctrinale</td><td>2002</td></tr><tr><td>CSEL</td><td>Augustine - Liber qui appellatur Speculum</td><td>1795</td></tr><tr><td>PL</td><td>Martinus Legionensis - Sermones</td><td>1735</td></tr><tr><td>PL</td><td>Auctor incertus - Breviarium</td><td>1625</td></tr><tr><td>PL</td><td>Ivo Carnotensis - Decretum</td><td>1613</td></tr></tbody></table>

## 4.3. Limits

As our approach relies on the automatic alignment of raw ATR output with digitized editions, several sources of noise may affect the resulting annotations regarding the task definition. First, since the distance between an abbreviated token and its expanded form influences alignment quality, there is a non-negligible probability that a manuscript form is aligned with a different lexical variant in the critical edition. This situation is common in textual transmission and may result in semantically or syntactically divergent alignments. Additionally, over-normalization may also occur, in a manner similar to variant-swapping phenomena, such as «<f>/<ph>» alternations in Latin or «<moult>/<molt>» variation in Old French. These cases may lead to normalized forms that contradict the intended task definition.

## 4.4. Description

Unsurprisingly, classical, late-antique, and Christian “literary” Latin texts are over-represented in the alignments between our source corpora and CoMMA, while later medieval administrative texts are under-represented. This reflects the fact that CoMMA is largely derived from library collections rather than archives. Patrologia Latina, our largest source (97M tokens, Roelli and Ctibor (2024)), accounts for 64.5% of the alignment pairs. By contrast, CEMA, an archival corpus and the second-largest source (57M tokens, Perreaux (2021)), represents only 1.5% of the alignments, whereas Perseus (6.1M tokens, Crane et al. (2021b)) accounts for 6.5%.

As expected from studies on manuscript transmission, some works are much more represented than others (see Table 4.4), and all most common authors are either tied to religious practice (missals, sermons and homilies), theory (commendaries), or canon law (decretum). One reason for this over-representation of some texts might be the presence of numerous bible citations.

Over-normalization, in the form of variant insertion, spelling standardization (in Latin), or lexical replacement (in Old French), constitutes one of the main identified risks. At this stage, however, we are unable to reliably distinguish over-
<figure><img src="image_3.png" /><figcaption>Figure 2: Distribution of the percentage of tokens per alignment in the train set that do not correspond to abbreviation resolution pattern.</figcaption></figure>

normalization from two related phenomena: first, cases in which abbreviation markers are not recognized by the ATR system (e.g. «ifusa» for «îfusa» with missed diacritics), and second, post-ATR editorial corrections, which are likely more prevalent.

Nevertheless, we estimate the extent of over-normalization by automatically aligning tokens in the training set and measuring the proportion of tokens that differ between the source and aligned texts when no explicit abbreviation markers are present. This analysis reveals a marked contrast between the two languages (Figure 2). In Latin, 20.5% of tokens exhibit substitutions consistent with non-standard abbreviation resolution. This proportion broadly aligns with expectations based on the 9.7% character error rate (CER) reported for CoMMA, including additional effects from the limited possibilities of over-normalization in Latin and variant substitutions.

By contrast, Old French displays a substantially higher level of non-abbreviation related substitutions, with a median substitution rate of 51% (and a mean of 50%). This discrepancy suggests that, compared to Latin, Old French editions impose a greater degree of spelling normalization on manuscript sources, thereby introducing more extensive spelling variation between manuscripts and their corresponding editorial representations.

## 4.5. Manual Correction and Gold Standard

To create a gold set, we sampled some data to be representative of main corpora from the automatic alignment. The resulting pairs are all manually corrected by a philologist to resolve alignment errors, normalization inconsistencies, and segmentation issues. This results in a gold-standard dataset

<table><caption>Table 2: Distribution of languages, sample counts, and token counts across the two alignment sources: raw ATR output and ground truth (GT).</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th colspan="4">Word Length</th></tr><tr><th>Samples</th><th>Average</th><th>Std</th><th>Median</th></tr></thead><tbody><tr><td rowspan="2">Raw ATR</td><td>French</td><td>189</td><td>95</td><td>34</td><td>89</td></tr><tr><td>Latin</td><td>352</td><td>93</td><td>39</td><td>88</td></tr><tr><td rowspan="2">GT ATR</td><td>Latin</td><td>45</td><td>288</td><td>256</td><td>259</td></tr><tr><td>Mixed</td><td>2</td><td>276</td><td>101</td><td>276</td></tr></tbody></table>

that serves both for evaluation and for qualitative error analysis. To that test data, we add ground truth data from CATMuS data repositories (both archives and literary documents) that was manually normalised by the same expert.

In the automatically aligned ATR output-part of the gold dataset, two texts are substantially over-represented: *Oresme* (75 alignments) and the *Histoire Ancienne* (43). This imbalance reflects the limited diversity of available Old French datasets, which made it challenging to construct a fully balanced sample. All other corpora are represented by between 11 and 21 alignments. Among these, some technically demanding texts are included, such as the *Mathematica* corpus. It is represented by 20 alignments and contains atypical elements such as geometrical point labels (e.g. ‘A’), which resemble abbreviations but must not be treated as such.

The ground truth ATR focused specifically on two series of texts: the first are drawn from archives, small letters and charters, mostly from the Cartularies of Notre-Dame de Paris (Claustre and Smith, 2022); the second are drawn from a single manuscript bearing the text *Liber de Coitu* (Constantine the African, xi c.). The first series contains administrative text, with various anonymous people and occasional switching between Latin and French, while the second contains very abbreviated content, such as “elīqṭʾ hūor ḫ ē.ī cʿēbʾ ʾ elīqʾ?” (corresponding to *eliquatur humor qui est in cerebro, et aliquatus* ‘the humor that is in the brain liquefies, and once it is liquefied’).

The final gold dataset comprises 588 samples (64,230 tokens) from 316 different manuscripts. 47 alignments were drawn from ground truth ATR, with no or very few recognition errors, and which are therefore longer than other samples (287 tokens on average vs. 94, see Table 4.5).

## 5. Benchmarking Normalization

Our first experiment evaluates the impact of the proposed dataset in the context of ATR output normalization. Specifically, we train a model for abbreviation resolution and compare its performance
against off-the-shelf approaches, including zero-shot and domain-limited pretrained models.

## 5.1. Setup

We formulate text normalization as a sequence-to-sequence task. We select ByT5 as the base architecture, as it can handle a wide range of characters (including Unicode-decomposed forms), since it operates at the byte level. This property has been associated with improved performance on languages exhibiting high levels of spelling variation (Wolters and Van Cranenburgh, 2024). ByT5 has also been successfully applied to related tasks such as ATR correction (Pavlopoulos et al., 2024), medieval Latin normalization (Büttner, 2025), and abbreviation expansion (Schonhardt, 2025).

**Baselines** We consider two baseline systems. The first is a ByT5-based model trained on the dataset of Schonhardt (2025). This model relies on different Unicode-level guidelines for abbreviations and is strongly domain-constrained. Despite the shared scribal conventions for abbreviation between Old French and Latin, we expect this model to perform poorly on Old French, which is out of domain. In addition, the dataset's source text were manually corrected, which may hinder robustness to ATR noise and to inconsistencies in spacing.

Our second baseline consists of zero-shot prompting Llama 3 8B Instruct (Grattafiori et al., 2024). The model is prompted to perform PEN, with an auxiliary instruction to predict the input language, and common abbreviation patterns and glyphs (Appendix D).

**Training data** The training data is bilingual, combining Latin and Old French. Owing to a strong imbalance between the two languages and the relative scarcity of Old French data, we upsample the French portion of the training set by a factor of ten, reducing the imbalance from approximately 100:1 to 10:1 (Latin to Old French), and thereby ensuring sufficient exposure to Old French during training.

**Experimental design** We evaluate on our manually curated test set, as well as on the first 2,000 entries of the dataset introduced by Schonhardt (2025), for which the Schonhardt baseline model should be at an advantage. All experiments are evaluated using CER and word error rate (WER), using the Levenshtein edit distance normalized over the length of the ground truth.

## 5.2. Results

Evaluated on our gold dataset, our proposed model performs well for both languages, improving over the zero-shot model and the only other

<table><caption>Table 3: Comparison of PEN results.</caption><thead><tr><th rowspan="2">Model</th><th colspan="3">CER</th><th colspan="3">WER</th></tr><tr><th>All</th><th>Latin</th><th>Old French</th><th>All</th><th>Latin</th><th>Old French</th></tr></thead><tbody><tr><td colspan="7" style="text-align: center;">On our gold data</td></tr><tr><td>Llama 3</td><td>139.9</td><td>160.4</td><td>79.9</td><td>163.7</td><td>183.1</td><td>107.3</td></tr><tr><td>ByT5 (Schonhardt)</td><td>64.8</td><td>75.6</td><td>33.3</td><td>92.3</td><td>104.8</td><td>55.9</td></tr><tr><td>ByT5 (Ours)</td><td><strong>6.7</strong></td><td><strong>6.3</strong></td><td><strong>7.6</strong></td><td><strong>20.7</strong></td><td><strong>19.6</strong></td><td><strong>23.8</strong></td></tr><tr><td colspan="7" style="text-align: center;">On Schonhardt data (Latin only)</td></tr><tr><td>Llama 3</td><td>26.4</td><td></td><td></td><td>54.5</td><td></td><td></td></tr><tr><td>ByT5 (Schonhardt)</td><td>130.0</td><td></td><td></td><td>109.4</td><td></td><td></td></tr><tr><td>ByT5 (Ours)</td><td><strong>11.1</strong></td><td></td><td></td><td><strong>38.5</strong></td><td></td><td></td></tr></tbody></table>

available model for the task by significant margins (Table 3). On the Schonhardt dataset, our model shows a stronger performance than the original model itself, the original model showing a tendency to undergenerate.

Qualitative analysis reveals a recurrent *classical bias* in the normalization of Latin, whereby the model tends to favor forms aligned with standardized spelling over historically attested medieval variants, except for final «-e» instead of «-ae». This tendency is illustrated by systematic preferences for «ph» over «ß» and for «-tio-» over «-cio-», reflecting a reversion to classical spellings. It also over-predicts names without context knowledge, such as «Johannis» for «Io.» and often struggles to adapt abbreviation resolution to the context, not unlike some of the models of Torres' work.

In Old French, the absence of stabilized orthographic conventions similarly leads the model to privilege dominant variants in its training set, normalizing forms such as «leurs» to «lors» and «pueple» to «pople». This results in the systematic erasure of localized *scripta* traditions, as the model prioritizes alignment targets over historically meaningful scribal variation. It however shows a strong capacity to retokenize or correct wrong ATR predictions.

Finally, we observe cases of variant insertion by the model, or at least semantic failure such as «iustum» for «iterum». While this phenomenon constitutes a limitation of the baseline model introduced in this study, it nevertheless highlights the importance of decoupling PEN from the ATR process itself. From a data-centric perspective, this separation enables us to trace normalization errors back to pre-editorial stages without requiring direct recourse to the original manuscript sources.

## 6. Impact on a Downstream Task

Given these results and the observed overnormalization effects, a remaining question is whether our normalization model—trained on the alignment dataset—can nevertheless outperform a previously established pipeline for downstream text analysis. To address this question, we compare our approach with the pipeline proposed by Clérice and Pinche (2025) in the context of stylometric au-
<table><caption>Table 4: Impact of the normalizer on downstream tasks (PoS tagging and lemmatization).</caption><thead><tr><th>Pipeline Name</th><th>Pre.</th><th>Lemma Rec.</th><th>F1</th><th>POS Pre.</th><th>POS Rec.</th><th>F1</th><th>POS 3-grams Prec.</th><th>Rec.</th><th>F1</th></tr></thead><tbody><tr><td colspan="10" style="text-align: center;">All tokens</td></tr><tr><td>Lambert</td><td>88.2</td><td><b>89.4</b></td><td>88.8</td><td>95.8</td><td><b>97.2</b></td><td>96.5</td><td>85.6</td><td><b>86.8</b></td><td>86.2</td></tr><tr><td>Ours</td><td><b>90.9</b></td><td>89.0</td><td><b>89.9</b></td><td><b>98.0</b></td><td>95.9</td><td><b>96.9</b></td><td><b>87.7</b></td><td>85.8</td><td><b>86.7</b></td></tr><tr><td colspan="10" style="text-align: center;">100 most frequent words (MFW)</td></tr><tr><td>Lambert</td><td>92.0</td><td><b>92.6</b></td><td><b>92.3</b></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Ours</td><td><b>94.3</b></td><td>89.5</td><td>91.8</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

thorship analysis of *Vie de Saint Lambert*, focusing on the extraction of lemmas and PoS information, including PoS 3-grams.

## 6.1. Setup

Stylometric analysis of Old French texts is strongly hindered by spelling variation, particularly in inter-manuscript settings. Their pipeline addresses this issue by combining a convolutional neural network for space error correction with a model for both PoS-tagging and lemmatization (Clérice et al., 2019) fine-tuned on artificially abbreviated data (hereafter, the *Lambert* pipeline). On the other hand, our pipeline uses our normalization model and the same PoS-tagger/lemmatizer from (Clérice et al., 2019).

**Data** We conduct our evaluation on their transcription of *Vie de Saint Lambert* in the manuscript BnF Fr. 412. We compare their lemmatization and PoS-tagging outputs and the annotations produced by our own pipeline against the ground truth annotation of the normalized version. The *Vie* displays a very low amount of contextual abbreviation, and is mostly hindered by spacing issues and ATR errors. We compare the ground-truth based on the full results and on the 100 most frequent words (MFW), in a setting similar to stylometry practices (ignoring any punctuation).

**Evaluation** To assess the impact of these different preprocessing pipelines, we adopt a Bag-of-Words (BoW) evaluation framework and compute precision and recall in order to measure their suitability for frequency-based analyses. Given *G* and *P* the sequences of gold and predicted labels, and $c_G(x)$ and $c_P(x)$ the number of times a label *x* occurs in *G* and *P*, we define the number of true positives as $TP = \sum_x \min(c_G(x), c_P(x))$. We then have $Precision_{BoW} = \frac{TP}{|P|}$, and $Recall_{BoW} = \frac{TP}{|G|}$.

## 6.2. Results

Overall, both models produce a comparable number of tokens. The Lambert pipeline generates approximately 2% more tokens, whereas the ByT5-

based approach results in 3.8% fewer tokens than the edited text. For both systems, our error analysis reveals that annotation errors are not directly correlated with the correctness of the normalization step applied to individual tokens. For instance, the word “uie” (life), which is a non-ambiguous input for manual lemmatization, is poorly lemmatized 66% of the time by the Lambert pipeline and 30% by ours, with two out of three errors due to a non-normalization to “vie” and a lemmatization to uie instead of vie1.

The results (Table 4) exhibit a consistent general trend across all evaluated subsets. Precision and F1 are systematically higher for our approach, within a few percentage points, while recall is consistently higher for the Lambert pipeline by a comparable margin (except for MFW lemmatization). Our model introduces less noise than Lambert’s pipeline but undergenerates and produces inadequate content for the tagger: for example, it does not produce apostrophes (“qu il” instead of “qu’il”), whereas they seem to be an overused marker by the tagger.

In the context of stylometric analysis, both approaches therefore present complementary advantages, depending on their respective impact on the distribution of the 100 most frequent words. The advantage of our approach lies in a potential simplification of the pipeline, contrary to the multiplication of steps and ad-hoc models from Lambert’s.

## 7. Discussion and Conclusion

We introduced pre-editorial normalization (PEN) as an intermediate task between graphemic ATR output and fully edited (normalized) text, and presented a large-scale aligned dataset for medieval Latin and Old French. Our experiments show that models trained on this resource substantially outperform existing baselines on normalization, and that PEN shows promising results as a first step for downstream linguistic analysis while simplifying processing pipelines.

At the same time, our results highlight persistent risks of over-normalization and editorial bias, particularly in the tendency to favor more classical spellings for Latin and alternative ones for Old French. These findings underline the importance of treating normalization as an intermediate layer rather than an implicit part of transcription.

Future work will focus on expanding the dataset, modeling uncertainty in normalization, and more importantly page-wide and document-level normalization settings, in line with recent practices in machine translation to use broader contextual windows. We hope that this resource and task definition will support more transparent and reusable approaches to research on historical texts.
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## A. Dataset and models

The ByT5 model can be found on HuggingFace at https://huggingface.co/comma-project/normalization-byt5-small.

The dataset is available on HuggingFace at https://huggingface.co/datasets/comma-project/pen-alignment-pairs.

## B. Corpus Used for the Alignment

<table><caption>Table B.5: Normalized corpus used by CoMMA for the ModernBert model.</caption><thead><tr><th>Corpus (Latin then French)</th><th>#kTokens</th></tr></thead><tbody><tr><td>Corpus Corporum (div.) (Roelli and Ctibor, 2024)</td><td>96,995</td></tr><tr><td>CEMA (Perreaux, 2021)</td><td>57,601</td></tr><tr><td>CAMENA (Schibel et al., 1999–2008)</td><td>7,916</td></tr><tr><td>(Assmann and Sahle, 1826–)</td><td>6,930</td></tr><tr><td>DigilibLT (Tobacco et al., 2021)</td><td>6,674</td></tr><tr><td>Perseus (Crane et al., 2021b)</td><td>6,148</td></tr><tr><td>CSEL (Crane et al., 2021a)</td><td>6,046</td></tr><tr><td>CroaLa (Jovanović, 2024)</td><td>5,233</td></tr><tr><td>Mirabile (Pinelli et al., 2023)</td><td>4,655</td></tr><tr><td>Bib. Italiana (Russo, 2023)</td><td>4,520</td></tr><tr><td>ALIM (Stella et al., 2025)</td><td>4,284</td></tr><tr><td>Add. Texts (Clérice, 2021)</td><td>3,844</td></tr><tr><td>Scripta (Bauduin et al., 2023)</td><td>2,226</td></tr><tr><td>Aposcripta (Théry, 2022)</td><td>1,736</td></tr><tr><td>ALCAR (Stutzmann et al., 2021)</td><td>973</td></tr><tr><td>Dig. Ptolemaeus (Hasse, 2023)</td><td>880</td></tr><tr><td>CGLO (Goetz and Gitner)</td><td>855</td></tr><tr><td>SourceEncyMe (Draelants and Kuhry, 2020)</td><td>777</td></tr><tr><td>NCA Dees et al. (2006)</td><td>3,185</td></tr><tr><td>BFM (Guillot et al., 2022)</td><td>6,293</td></tr><tr><td>Fabliaux (Pierreville et al., 2025)</td><td>120</td></tr><tr><td>Geste (Camps et al., 2019)</td><td>324</td></tr><tr><td>DocLing (Glessgen et al., 2016)</td><td>1,278</td></tr><tr><td>TVOF (Morcos et al., 2020)</td><td>361</td></tr><tr><td>OFC (Sneddon, 1982)</td><td>14</td></tr><tr><td>Total</td><td>229,870</td></tr></tbody></table>

## C. Ranking of Corpora according to the Number of Alignments

<table><caption>Table C.6: The most frequent aligned corpora in the dataset.</caption><thead><tr><th>Corpus</th><th>#alignments</th></tr></thead><tbody><tr><td>Patrologia Latina</td><td>2,838,542</td></tr><tr><td>Lasciva Roma</td><td>413,580</td></tr><tr><td>CSEL</td><td>377,601</td></tr><tr><td>PerseusDL</td><td>306,297</td></tr><tr><td>DigilibLT</td><td>179,899</td></tr><tr><td>Auctores Scientiarum Varii</td><td>110,535</td></tr><tr><td>CEMA-latin</td><td>78,570</td></tr></tbody></table>

## D. Llama3 8B Prompt

**system:** You are a specialist in Medieval Paleography and HTR correction.

Your goal is to transform "raw" HTR transcriptions into normalized Medieval Old French or Latin.

### LINGUISTIC & PALAEOGRAPHIC RULES:

* **HTR ERROR CORRECTION:** Fix character confusions typical of HTR, such as 'm' being misread for 'in' (e.g., 'mexper' -> 'inexper') or 'f' for 's'.

* **U/V & I/J NORMALIZATION:** Distinguish between vocalic and consonantal uses. Use 'v' and 'j' for consonants and 'u' and 'i' for vowels.

* **EXPANSION:** Fully expand all scribal abbreviations and suspensions into their full character forms.

* **GRAPHICAL VARIATION:** Fully respect graphical variation, such as filosofia should be kept instead of normalizing to philosophia.

* **APOSTROPHE:** Add apostrophe where necessary (e.g., 'dun' -> 'd'un').

* **SPACES:** Remove extra spaces (e.g., 'coeur' -> 'coeur') and add necessary ones (e.g., 'letriste' -> 'le triste').

### ABBREVIATION KEY:

* 9 or 9 = com / con (e.g., 9me -> comme)

* 9 = us (e.g., vo9 -> vous)

* ḫ = qui

* ḫ = ol (e.g., mlt -> molt)

* ḫ (tilde/macron) = nasalization (n/m) or missing letters (e.g., ḫ -> que)

* vertical tilde for suspension (missing letters) or R based phonemes (e.g., -re/er/-ra/ar-)

Return ONLY JSON.

**user:** Normalize this HTR output: text

**assistant:**