# CoMMA, a Large-scale Corpus of Multilingual Medieval Archives

Thibault Clérice, Simon Gabay, Malamatenia Vlachou-Efstathiou, Ariane Pinche, Benoît Sagot

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Thibault Clérice, Simon Gabay, Malamatenia Vlachou-Efstathiou, Ariane Pinche, Benoît Sagot. CoMMA, a Large-scale Corpus of Multilingual Medieval Archives. 2025. hal-05299220v2

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# CoMMA, a Large-scale Corpus of Multilingual Medieval Archives

Thibault Clérice¹, Simon Gabay², Malamatenia Vlachou-Efsthatiou³,
Ariane Pinche⁴, Benoît Sagot¹

(1) Inria, Paris, France. name.surname@inria.fr

(2) Université de Genève, Geneva, Switzerland. name.surname@unige.ch

(3) IRHT/CNRS – ENPC, Paris, France. name.surname@enpc.fr

(4) CNRS, Lyon, France. name.surname@cnrs.fr

## Abstract

We present CoMMA, a large-scale corpus of medieval manuscripts produced through automatic text recognition. The corpus contains around 3.3b tokens drawn from more than 32,700 digitized manuscripts in Latin and Old French, harvested via IIIF. Unlike other resources, it is made of raw, non-normalized text enriched with layout analysis in various formats. We describe the pipeline used for large-scale acquisition and processing, and report quantitative and qualitative evaluations (average CER 9.7%). The resulting resource supports multiple use cases, from pretraining language models to corpus linguistic on historical languages and digital humanities applications.

**Keywords:** Automatic Text Recognition, Medieval manuscripts, Latin, French, Digital humanities, Corpus

## 1. Introduction

Estimates suggest that around 1.3 million manuscripts from the Latin West produced before 1800 still survive today (Buringh, 2010). The Biblissima IIIF-Collection (Morlock et al., 2025) currently provides metadata for approximately 143,000 digitized manuscripts, including some 65,000 described as Latin and 18,500 in pre-modern varieties of French. Despite this wealth of material, accessible large-scale datasets for Old French and medieval Latin remain scarce. For computational linguists and NLP practitioners, most available resources are limited in scope, editorialized, and heavily normalized for readability (including but not limited to punctuation, capitalization, word segmentation, and finally abbreviation resolution), which obscures the original manuscript practices of abbreviation, “misspellings”, and orthographic variability.

For Old and Middle French, spelling variation is not accidental but structural. Prior to the orthographic stabilization that spread gradually from the Renaissance (Gabay and Clérice, 2024), spelling practices themselves functioned as markers of place and period of production. Dees (1985) pioneering quantitative work on Old French dialects highlighted these regional patterns. Yet, Morin (2006) has shown that reliance on critical editions introduced distortions: in “Floovant”—one of the texts produced by Dees and later captured in the Nouveau Corpus d'Amsterdam (NCA Stein and Kunstmann 2006)—a corpus resulting of Dees work, Morin showed that the ‘r’ sign from the manuscript is expanded as *et* ('and') in the edition while used in the coordinating function, ignoring the fact that ‘r’ is also used in place of *e* or *a* in prepositional contexts (modern French à ‘at’), lead-

ing him to believe that the normalization *e* is as valid as *et*, potentially changing relative frequencies of both forms. Printed editions and their digitization, which comprise the vast majority of the digital corpora available online, conceal at best – erase at worst – both the abbreviation practice and the choice made by the editor, which sometimes reinforce or suppress local spellings. For computational philology and corpus linguistics (CL), this raises a key issue: statistical conclusions drawn from such corpora may be skewed by this practice.

Recent advances in automatic text recognition (ATR) have made it possible to address this challenge at scale. For medieval manuscripts in Western languages, recognition accuracy is increasingly limited, not by models but by the availability of ground-truth data, with current error rates often below 10%. Initiatives such as the CATMuS Medieval dataset (Clérice et al., 2024) have provided training material across 9 centuries (8-16th) and ten western languages written in Latin script, covering a range spanning from administrative registers to hagiographic texts. This dataset opens the way to large-scale corpus creation directly from manuscript images and keeps abbreviations in its transcription ground truth.

In this paper, we present the CoMMA corpus, a new large-scale resource built from 32,763 digitized manuscripts, totaling 3.33 billion tokens. To our knowledge, this is more than 12 times the size of the largest open Latin corpus currently available (2.7B vs. 226M, Corpus Corporum, Roelli and Ctor, 2024), and represents more than 40 times the open Old French as well (516.8M vs 11.2M). Unlike most existing corpora, the CoMMA corpus retains abbreviation signs, preserving the diversity of manuscript practices.

Our main contributions are threefold:
* **A scalable pipeline** for corpus construction from manuscript digitizations, combining ATR with layout analysis using the SegmOnto controlled vocabulary (Gabay et al., 2024);

* **The CoMMA corpus itself**, comprising both raw text and semi-structured output using XML-TEI, openly available for NLP and corpus linguistics research;

* **Evaluation and applications**, including character error rate (CER) benchmarks, lexical overlap measures, and example use cases in language modelling, historical linguistics, and manuscript layout analysis.<fnref n="1" />

The remainder of this paper is structured as follows. In section 2, we review related work on historical language corpora and large-scale ATR initiatives. In Section 3, we describe the corpus sources, coverage, and processing pipeline. In section 4, we provide metrics regarding the corpus and evaluate its quality and metadata. In section 5, we present three example applications on the corpus: (1) analysis of the evolution of book layout practices, (2) analysis of the evolution of scribal practices, and (3) pretraining of a Modern-BERT model (Warner et al., 2024) for semantic manuscript exploration. Section 6 concludes with future directions for extending and annotating the corpus.

## 2. Related Work

### 2.1. Large Corpora in Old French and Latin

Corpus building for Latin traces back to the pioneering work of Roberto Busa, one of the first to systematically digitize textual resources (Busa, 1980). Since then, numerous large-scale textual corpora have been produced, ranging from linguistically annotated datasets, such as LASLA's dataset—the largest annotated Latin corpus—to simply readable text collections (Denooz, 1996). Many of the largest corpora, however, remain behind paywalls (e.g., LLT-A and LLT-B, Centre Traditio Litterarum Occidentalium 2025, FranText, Montémont 2020) or accessible only through controlled reading environments (e.g., NCA at ATILF, Stein and Kunstmann 2006). This restricted access limits potential uses, from vectorization to pretraining language models. Other open projects, such as CEMA Perreaux (2021), while freely accessible, do not yet support large-scale computational usage and often require manual download of individual files, making large-scale processing cumbersome.

Openly licensed and easy-to-download corpora for Latin and Old French do exist. Examples for Latin include the Perseus Digital Library for the classical period (Crane et al., 2021b), DigilibLT for late antiquity Latin (Tabacco et al., 2021), and the CSEL through the Open Philology Project (Crane et al., 2021a) for Christian Latin. The Corpus Corpus (Roelli and Ctibor, 2024) provides a unified entry point to many of these resources. For Old French, the research community is smaller, but several open-access corpora exist, including the Base de Français Médiéval (BFM, Guillot et al. 2018) as well as DocLing for charters (Glessgen, 2017). These resources amount to only a few hundred million tokens: 10 million tokens for Old French, 220 million for Latin.

The development process of the first Latin BERT model (Bamman and Burns, 2020) illustrates the limitations of existing corpora: to reach sufficient scale for pretraining, the authors relied on uncorrected OCR from Archive.org, adding 560 million tokens from uncurated and uncorrected OCR to a smaller yet curated corpus of 81M tokens produced by various Digital Humanities (DH) projects.

Moreover, a common characteristic of these projects is that they mostly rely on printed editions as their primary source. While convenient, this practice smooths the inherent diversity present in manuscript transmission and often normalizes original writing practices. For instance, in the document identified as RM 1291 06 09 01 from DocLing—a project with digital born editions—the manuscript spelling «cest asauoir ḫlsūt» is normalized as “C’est à-savoir qu’il s’unt” (italic identifies resolved abbreviation). This version of the text obscures the manuscript’s variability: there is no indication that the normalization of spacing (or retokening) originates with the scientific editor and thus constitutes an editorial intervention. Such interventions underscore the challenge of capturing genuine manuscript variation — and, by extension, linguistic variation — in computational corpora.

Recent advances in ATR have enabled the creation of large-scale corpora from historical manuscripts. Notably, the Open Islamicate Texts Initiative (OpenITI, Miller et al. (2018)) provides a machine-actionable corpus of Persian and Arabic texts, while projects such as MiDRASH (Stoekl Ben Ezra et al., 2025) aim to release similar corpora for other languages.

### 2.2. Layout Analysis

Layout analysis is the process of detecting and describing the structural elements of a page, such as text regions and lines, to support automatic text recognition. While this task is well studied for modern documents, medieval manuscripts pose additional challenges due to irregular page struc-

<footnote n="1">¹All will be made available upon publication.</footnote>
## 3.2. Production Pipeline

In order to produce the corpus from the 33,000 manuscripts, we developed a pipeline able to deal with both the scale and the instability of the downloading process. The workflow proceeds in three stages. First, the metadata manifest of each manuscript is retrieved and cached. Second, images are downloaded with a maximum height of 2,500 pixels, following the practice established in the CATMuS datasets, and are processed in parallel while respecting the constraints imposed by the providers. Then, layout analysis and recognition were carried out using two different libraries: YOLOv11 (Khanam and Hussain, 2024) for layout segmentation and Kraken for ATR, both outputting ALTO-XML. YOLOv11 was run with a model trained on CATMuS Medieval Segmentation open data, using the SegmOnto vocabulary for regions and lines (Mattingly, 2025). Kraken employed the CATMuS 1.6.0 model (Pinche et al., 2025). As a result of our model choices, our corpus follows the CATMuS guidelines, as closely as the models use apply them faithfully.

Quality safeguards were introduced at each stage, verifying: (1) image readability, (2) XML existence (indicating that layout analysis was at least performed), (3) XML parsability, and (4) presence of text predictions within the XML. Any failure triggered reprocessing. Once a manuscript reached 100% page coverage, the XML files were archived along with a custom manifest recording page order and original IIIF manifest URI.

ALTO files were subsequently converted into TEI-XML pages, focusing exclusively on textual content (i.e., without spatial coordinates or links to the original image). A mapping was defined to align SegmOnto categories with TEI structures: MainZone was rendered as <ab> blocks; DefaultLine as plain text lines; and HeadingLine as <span class="rubricated">>, while page change is indicated through <pb>. Other content was mapped to specific elements, such as <note> for marginal or interlinear text, and <fw> for catchwords or running titles. This design enables flexible exports: for instance, the main body of a manuscript can be obtained by simply ignoring note and fw tags, while it is equally possible to export only the notes. To facilitate usage, each manuscript is released with the TEI files, the corresponding ALTO files, JSON files with layout information and text from the original documents, and a pre-extracted main text version, sparing users from the need to write XML-handling scripts.

## 4. Resulting Corpus

### 4.1. A Corpus with Abbreviations

An essential feature of the CATMuS guidelines is the normalization of letter variants to ensure consistency across sources and languages. Abbreviations are preserved in their original form, encoded in Unicode Normalization Form Decomposed (NFD), and supplemented with special characters registered through the Medieval Unicode Font Initiative (MUFI). A limited number of characters from the Unicode Private Use Area are also employed, such as « (U+F1AC), defined in the MUFI, which—despite its resemblance to a semicolon—is conventionally used for abbreviating -ed or -ibus at the end of words. Graphical alternations that lack linguistic contrast, such as u/v or i/j, are collapsed into a single form (u/i).

The CATMuS guidelines also normalize spacing around word boundaries, as manuscript spacing is often ambiguous or inconsistent. Punctuation is restricted to two sentence-level signs (« and »), but several additional content or typographic markers are retained: hyphenation («-»), content shifts («¶), note insertions («^), and token separation («/»). To illustrate these conventions, Table 1 presents 10 randomly sampled lines from the main body of text in five Latin and five French manuscripts.

### 4.2. Statistics

**Corpus Overview** Out of the 49,443 queried digitizations, the corpus contains 32,763 distinct manuscripts. Over the 32,763 manuscripts, 25,270 are labelled as Latin only, 5,611 as French, 1,120 as bilingual, 413 as Italian, 288 as mixing either Latin or French with another language and 59 as trilingual. The average number of digitized images per unique manuscript is 172 ($\sigma$ = 175), the number of lines per manuscript is 15,123 ($\sigma$ = 21,148), while the average number of white-space-delimited tokens is 101,649 ($\sigma$ = 143,801). The distribution of manuscripts and word counts across centuries is shown in Table 2 and 3. The total number of tokens is 2.33B.

**Language Classification of Manuscripts** To systematically categorize our manuscript data by language, we designed a rule-based classification using series of window-level language scores, with each window representing 1% of the tokens. We trained a FastText language identification model (Joulin et al., 2016) on the CATMuS Medieval dataset at the line level, using its ten predefined language classes. The training and development sets were used to train the model, while we used the test set to evaluate its performance. The resulting classifier achieved strong F1 scores for French
<table><caption>Table 1: 10 lines with different level of readability and abbreviation rate</caption><thead><tr><th></th><th>Latin</th><th>French</th></tr></thead><tbody><tr><td>Readable</td><td>Sanguis : in altari sumit' ipse manës.<br/> fabrica nîra p h quod scdo sonuit fci ê<br/> Sulatis qs dîne placare munerib⁹ & inî cedëtib; sistuis.<br/> potestis q int⁹ istus iinebiras diffinlior ad<br/> xxuii E. Obitus thome de benoist qui dedit fabrice huius ecche</td><td>et depute nos Baillifs et Seneschaux sur les<br/> ¶Imôseigneur quât tes faiz ie recorde<br/> comptes du faut delas tresori/ en son propre nom p'g<br/> Souspirant. ex criant ex ne ueuxe point tascher<br/> Q qui pe jmere des honeure</td></tr><tr><td>Too noisy</td><td>AiU<br/> Tonclisio ebri et siguario dap a<br/> pgem9 dei fficie opa. nôg sno psmeno<br/> ol reg deaê<br/> OParencû omor l'i .ir. c'a.h</td><td>Iour de nommbir lan nulmy ourr ey Rend<br/> R mouerubut e<br/> Sl le puueast Re fio<br/> uons priiat fayr mi pomimuattan du<br/> dee uauy dal l heuants Regaois auspant</td></tr></tbody></table>

<table><caption>Table 2: Distribution of manuscripts across centuries according to original metadata. *Ot* stands for Others.</caption><thead><tr><th>Century</th><th>9</th><th>10</th><th>11</th><th>12</th><th>13</th><th>14</th><th>15</th><th>16</th><th>Total</th></tr></thead><tbody><tr><td>Fr</td><td>0</td><td>0</td><td>8</td><td>24</td><td>511</td><td>796</td><td>2410</td><td>1862</td><td>5611</td></tr><tr><td>Fr, La</td><td>5</td><td>7</td><td>7</td><td>55</td><td>120</td><td>179</td><td>634</td><td>113</td><td>1120</td></tr><tr><td>Fr, La, Ot</td><td>0</td><td>1</td><td>0</td><td>5</td><td>10</td><td>10</td><td>23</td><td>10</td><td>59</td></tr><tr><td>Fr, Ot</td><td>0</td><td>0</td><td>0</td><td>1</td><td>11</td><td>11</td><td>20</td><td>11</td><td>54</td></tr><tr><td>It</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>54</td><td>208</td><td>149</td><td>413</td></tr><tr><td>La</td><td>1089</td><td>699</td><td>1153</td><td>4234</td><td>5659</td><td>4922</td><td>6133</td><td>1381</td><td>25270</td></tr><tr><td>La, Ot</td><td>14</td><td>10</td><td>12</td><td>21</td><td>17</td><td>30</td><td>93</td><td>37</td><td>234</td></tr></tbody></table>

<table><caption>Table 3: Token counts per century (M tokens). *Ot* stands for Others.</caption><thead><tr><th>Century</th><th>9</th><th>10</th><th>11</th><th>12</th><th>13</th><th>14</th><th>15</th><th>16</th><th>Total</th></tr></thead><tbody><tr><td>Fr</td><td></td><td></td><td>0.61</td><td>0.80</td><td>78.37</td><td>106.07</td><td>238.18</td><td>92.82</td><td>516.85</td></tr><tr><td>Fr, La</td><td>0.73</td><td>0.62</td><td>0.22</td><td>3.01</td><td>8.85</td><td>14.01</td><td>20.99</td><td>3.17</td><td>51.60</td></tr><tr><td>Fr, La, Ot</td><td></td><td>0.10</td><td></td><td>0.85</td><td>1.23</td><td>0.72</td><td>0.75</td><td>0.32</td><td>3.97</td></tr><tr><td>Fr, Ot</td><td></td><td></td><td></td><td></td><td>0.46</td><td>0.62</td><td>1.20</td><td>0.46</td><td>2.74</td></tr><tr><td>It</td><td></td><td></td><td></td><td></td><td>0.24</td><td>4.84</td><td>15.62</td><td>7.26</td><td>27.96</td></tr><tr><td>La</td><td>73.91</td><td>41.47</td><td>82.98</td><td>371.52</td><td>820.71</td><td>776.37</td><td>483.59</td><td>60.51</td><td>2711.06</td></tr><tr><td>La, Ot</td><td>1.04</td><td>0.62</td><td>0.46</td><td>2.05</td><td>2.09</td><td>4.13</td><td>4.08</td><td>1.64</td><td>16.11</td></tr></tbody></table>

and Latin (89.4% and 95.5%). Overall, the model attained an accuracy of 88.6% across the entire corpus.

For each manuscript, we computed smoothed summary features over a sliding window, including the mean and standard deviation for French, Latin, and an *Other* category ($P_{Other} = 1 - (P_{French} + P_{Latin})$), as well as the mean difference between Latin and French capturing their balance. These metrics formed the basis for a hierarchical rule-based classification scheme: manuscripts with highly dominant and stable French or Latin lines were labelled *Truly French* or *Truly Latin* ($\mu_{lang} > 0.9$ and $\sigma_{lang} < 0.05$). Those with a clear majority of one language but some variability were classified as *Massively French*, *Massively Latin* or *Massively Other* (threshold 0.7 and $\mu_{lang} > \mu_{other}$).

Manuscripts containing substantial proportions of both French and Latin were identified as *Truly bilingual* ($\mu_F + \mu_L > 0.8$ and $|\mu_F - \mu_L| < 0.2$) or *Massively bilingual* (thresholds 0.7 and 0.3). Manuscripts not fitting any of these criteria were marked as Ambiguous. This hierarchical approach captures both extreme homogeneity and mixed-

<table><caption>Table 4: Language in metadata vs. Language classification over the full content, as percentage.</caption><thead><tr><th></th><th>Bilingual<br>Mass.</th><th>Truly</th><th>French<br>Mass.</th><th>Truly</th><th>Latin<br>Mass.</th><th>Truly</th><th>Other<br>Mass.</th><th>Ambiguous</th></tr></thead><tbody><tr><td>Fr</td><td>0.5</td><td>0.6</td><td>35.8</td><td>58.2</td><td>0.6</td><td>0.1</td><td>0.9</td><td>3.3</td></tr><tr><td>Fr, La</td><td>3.4</td><td>2.9</td><td>20.0</td><td>5.0</td><td>54.6</td><td>5.8</td><td>0.4</td><td>7.9</td></tr><tr><td>Fr, La, Ot</td><td>1.7</td><td>5.1</td><td>23.7</td><td>6.8</td><td>30.5</td><td>8.5</td><td>8.5</td><td>15.3</td></tr><tr><td>Fr, Ot</td><td>1.9</td><td></td><td>25.9</td><td>13.0</td><td>1.9</td><td></td><td>22.2</td><td>35.2</td></tr><tr><td>It</td><td></td><td></td><td>0.5</td><td></td><td>0.5</td><td></td><td>93.5</td><td>5.6</td></tr><tr><td>La</td><td>1.1</td><td>0.6</td><td>1.4</td><td>0.1</td><td>41.6</td><td>50.8</td><td>0.3</td><td>4.1</td></tr><tr><td>La, Ot</td><td></td><td>0.4</td><td>1.3</td><td>0.4</td><td>36.8</td><td>19.2</td><td>17.1</td><td>24.8</td></tr></tbody></table>

language phenomena within the corpus, revealing insights that contrast with metadata labels from the metadata providers: Latin and French manuscripts are more often predominantly Latin or bilingual than the metadata suggests (see Table 4). On the other hand, around 90% of manuscripts labelled as “French” or “Latin” are not relabelled.

### 4.3. Evaluation

To assess the reliability and reusability of the corpus, we carried out a systematic evaluation of transcription quality. Our goal was to quantify recognition accuracy across languages and centuries, and to identify the main sources of error that limit downstream use. The evaluation combines quantitative metrics (character error rate, CER) with a qualitative analysis of common error patterns, thereby providing a comprehensive view of corpus performance.

**Sampling strategy** To assess the quality of the corpus, we conducted manual post-correction on a sample of 670 manuscripts. For each manuscript, three consecutive lines from a single digitized page were selected and their transcriptions were corrected independently by two expert annotators, one for each language, on eScriptorium (Kiessling et al., 2019). During annotation, each page could be flagged for issues such as bad metadata (e.g., incorrect century attribution, including 19th-century copies of earlier works, wrong language, or unsuitable material such as early printed books),
<figure><img src="image_2.png" /><figcaption>Figure 1: Dispersion of error by manuscript and centuries (CER as %).</figcaption></figure>

bad layout analysis (e.g., duplicated lines, incomplete lines, incorrect line direction), or marked for exclusion. Exclusion was applied either when metadata or layout problems made evaluation unreliable, or when the page was deemed too difficult to decipher.

Two groups of manuscripts were defined: one for those labelled as Latin, the other as French. Sampling was stratified by century to ensure temporal coverage, although the distribution of manuscripts across centuries differs between the two languages. When the number of available manuscripts in a given century fell below the target quota, all available manuscripts were included. For centuries with a surplus, the number of manuscripts was capped to maintain balance. As a result, the Latin set includes 52 manuscripts from the 9th century and about 40 per subsequent century. The Old French set contains 12 manuscripts from the 12th century and approximately 80 per century from the 13th onward.

**Quantitative Analysis** The overall character error rate (CER) across the sample is 9.7%, with Latin manuscripts performing better (8.4%) than French ones (11.05%). Only a small proportion of documents were excluded due to misclassified language, incorrect line detection, or extreme unreadability (11% for Latin, 10.5% for French). Accuracy varies across centuries (Table 5), with French manuscripts of the 12th century showing the widest dispersion of CER values (Figure 1), a trend further explained in the qualitative analysis.

The error distribution reflects well-known challenges in medieval OCR. As in the CATMuS benchmark, spacing errors dominate: 13.8% of all mistakes are insertions, deletions, or substitutions of spaces. Character-level confusions follow predictable palaeographic patterns—`<u/n>` substitutions accounts for 3% of errors, `<i/u>` for 2.1%, `<c/e>` for 1.6%, and `<i/r>` for 1.4%—aligning with the inherent ambiguities of medieval scripts.

<table><caption>Table 5: Average CER (%) per century. The table also reports the number of manuscripts included in CER computation (Support) and page-level annotations of issues: incorrect metadata, incorrect layout analysis, and exclusion from evaluation.</caption><thead><tr><th></th><th></th><th>9</th><th>10</th><th>11</th><th>12</th><th>13</th><th>14</th><th>15</th><th>16</th><th>Total</th></tr></thead><tbody><tr><td rowspan="2">French</td><td>CER</td><td></td><td></td><td></td><td>28.6</td><td>5.3</td><td>14.4</td><td>9.7</td><td>10.4</td><td></td></tr><tr><td>Support</td><td></td><td></td><td></td><td>11</td><td>77</td><td>68</td><td>73</td><td>70</td><td>299</td></tr><tr><td rowspan="3">Inc. Metadata</td><td>Inc. Metadata</td><td></td><td></td><td></td><td>6</td><td>12</td><td>12</td><td>7</td><td>10</td><td>47</td></tr><tr><td>Inc. LA</td><td></td><td></td><td></td><td>5</td><td>7</td><td>17</td><td>2</td><td>4</td><td>35</td></tr><tr><td>Exclusion</td><td></td><td></td><td></td><td>1</td><td>4</td><td>13</td><td>8</td><td>9</td><td>35</td></tr><tr><td rowspan="2">Latin</td><td>CER</td><td>7.6</td><td>6.7</td><td>8.9</td><td>5.6</td><td>10.6</td><td>8.7</td><td>9.5</td><td>11.2</td><td></td></tr><tr><td>Support</td><td>52</td><td>37</td><td>36</td><td>37</td><td>40</td><td>37</td><td>35</td><td>24</td><td>298</td></tr><tr><td rowspan="3">Inc. Metadata</td><td>Inc. Metadata</td><td>0</td><td>1</td><td>0</td><td>1</td><td>1</td><td>1</td><td>2</td><td>1</td><td>7</td></tr><tr><td>Inc. LA</td><td>1</td><td>4</td><td>8</td><td>1</td><td>0</td><td>3</td><td>1</td><td>1</td><td>19</td></tr><tr><td>Exclusion</td><td>4</td><td>3</td><td>2</td><td>3</td><td>2</td><td>3</td><td>5</td><td>15</td><td>37</td></tr></tbody></table>

**Qualitative analysis** Transcription quality depends primarily on the script type, the layout analysis accuracy, and, to a lesser extent, image quality.

In medieval palaeography, the notion of script type refers broadly to the characteristic style of handwriting used in a given period or region. The most represented script types within the CATMuS dataset, such as the caroline minuscule—an articulated and rounded style common in early medieval Europe—, the textualis—closer to our modern gothic typefaces—, and the hybrida—combining features of textualis and cursiva, a looped script type used especially for administrative or documentary purposes—are generally well recognized by the model. Systematic recognition errors occur naturally when the system encounters script types that are absent from the training data. For example, the presence of the mid-8th-century Beneventan script introduces errors due to the unfamiliar ligatures and letterforms employed.

Rapidly executed script type—specifically for the cursiva—and abbreviation rate preset greater challenges. A notable case mixing both is later French account manuscripts—records of financial transactions and inventories—typically written in fast cursiva, which yield particularly high error rates.

Errors linked to layout analysis are mostly due to inaccurate line segmentation, which can produce incomplete or merged lines—for example, when a single predicted baseline spans two or three consecutive lines and prevents correct separation and consequently recognition. Although less critical, low image quality still contributes to recognition errors in faint or small module characters.

# 5. Example Applications

## 5.1. Evolution of Layouts

As a first example of a DH application, we analysed the evolution of manuscript page layouts over time. A page in a manuscript is not only a container for text but also a structured space that organizes in-
formation and guides reading (Goody, 1977). Its
design evolved gradually, influenced by both copy-
ists and readers (Kwakkel, 2018), yet prior studies
were largely qualitative and could not quantify his-
torical patterns across large corpora (Zali, 1999;
Grafton, 2012; Vandendorpe, 2021).

<figure><img src="image_3.png" /><figcaption>Figure 2: Changes in the average structure of a manuscript page over the centuries (x-axis): smoothed average count of zones (left y-axis, coloured curves) and average number of tokens per page (right y-axis, black curve).</figcaption></figure>

Using our corpus, we quantified layout
complexity by analysing MainZone's (text),
MarginTextZone's (paratext), and the combina-
tion of DropCapitalZone's and GraphicZone's
(visual elements), alongside average tokens per
page as a proxy for page format (cf. Figure 2).

Three trends emerge: (1) oscillation between
mono- and multi-column layouts, with a decline of
the former in the 11th century; (2) a phase of in-
creased complexity, marked by more zones and
higher token density; (3) simplification towards the
Late Middle Ages. These patterns reflect both
material constraints, such as manuscript size and
cost, and cultural factors, including the influence
of scholasticism followed by humanist preferences.
This analysis demonstrates how large-scale dig-
itized corpora can enable quantitative studies of
manuscript layout evolution, complementing tradi-
tional humanities research.

5.2. Scribal Evolution

Complexity appears not only in the layout of the
page but also in the form of individual words. For
instance, the word *chevalier* (“knight”) could be ab-
breviated as «chlr» or «chŕ». Such abbreviations en-
tail a cost of decoding time: they are faster to write,
but longer to read (Perea et al., 2006).

Fig. 3 shows that Latin manuscripts display
a curve similar to that of page layout, peaking
in the 14th century and then declining. French

<figure><img src="image_4.png" /><figcaption>Figure 3: Percentage of abbreviated tokens per century (x-axis) for French and Latin.</figcaption></figure>

manuscripts, in contrast, demonstrate consistently
lower percentages, reflecting a different scribal tra-
dition despite—or perhaps because of—their in-
heritance of Latin abbreviations (Hasenohr, 1998).

When combined with the layout analysis, a gen-
eral trend emerges: after a phase of maximal
complexity around 1300, information presentation
gradually simplified toward the end of the period.
This pattern may reflect an attempt to optimize the
reader’s “cognitive load” (Sweller, 1988).

5.3. Contextual Embedding model

Finally, to evaluate the corpus as a resource for
NLP, we trained a contextual embedding model.
This both demonstrates the corpus's usability for
common NLP tasks and establishes a baseline
model for future downstream applications.

**Training Data Preparation** We assembled ed-
itorialized Latin and Old French data from multi-
ple sources (see Table 6). From these corpora, we
constructed a lowercased vocabulary, excluding
single-character tokens and relying on whitespace
tokenization. A SentencePiece tokenizer (NFD
mode, Kudo and Richardson (2018)) was then
trained jointly on the full clean dataset and a sub-
set of 23,000 manuscripts, with a size of 30,002
tokens, including special tokens.

The transformation of the source text into sam-
ples followed two distinct strategies. Manuscripts
were divided into non-overlapping sequences of
five lines. Edited texts, by contrast, were seg-
mented at strong punctuation marks, with a 30%
probability of ignoring the punctuation and a mini-
mum sequence length of 20 tokens (as defined by
the tokenizer). For validation, 5% of the edited ma-
terial (173k samples) was held out, with an equal
number of samples withheld from the manuscript
set, yielding a total validation set of 347k samples.
<table><caption>Table 6: Normalized corpus for ModernBert.</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 Tabacco 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>

The manuscript training data was further filtered using the vocabulary list derived above. Any manuscript sample with less than 30% vocabulary overlap was discarded, as these typically corresponded to heavily abbreviated or noisy ATR outputs. A manual check on 400 edited texts yielded an average overlap of 57%, supporting this threshold. Filtering resulted in removing 20% of manuscript samples, leaving 40.5 million usable sequences, supplemented by 3.3 million sequences from edited corpora. Finally, to balance the training signal, clean editorial samples were upsampled following the approach of Bamman and Burns (2020), ensuring that each epoch presented equal proportions of edited and ATR-derived data.

**Experimental Set-up** We trained a ModernBERT model using the HuggingFace library with default parameters, except for the vocabulary size and the mask replacement probability, which was changed to 98% to account for the inherent noise of the data. The model was trained for 10 epochs. Hyperparameter tuning and adjustments added the equivalent of 3 additional days of computation.

**Method** We selected two digitized edited texts and two manuscripts that were not part of the previously described datasets. From each sentence, we extracted 100 random segments. In each segment, a single token was randomly masked, and the model's predictions were evaluated against

<table><caption>Table 7: Masked filling accuracy over 4 new texts.</caption><thead><tr><th>File</th><th>Top 1</th><th>Top 5</th><th>Top 1 Var.</th><th>Top 5 Var.</th></tr></thead><tbody><tr><td>Fro (Edited)</td><td>54.4</td><td>68.9</td><td>58.3</td><td>70.9</td></tr><tr><td>Fro (ATR)</td><td>45.6</td><td>65.0</td><td>56.3</td><td>68.9</td></tr><tr><td>Lat (Edited)</td><td>53.4</td><td>72.8</td><td>55.3</td><td>73.8</td></tr><tr><td>Lat (ATR)</td><td>33.3</td><td>52.9</td><td>40.2</td><td>53.9</td></tr></tbody></table>

the top-1 and top-5 outputs. Expert annotators reviewed the results, taking into account potential historical spelling variants, which extended the scoring to include variant spellings.

**Results** The model demonstrated a strong ability to provide plausible token predictions, as shown in Table 7. Old French benefited the most from considering spelling variants (+3.9 for Top-1 in edited texts, +10.7 in ATR output). Interestingly, the model sometimes proposed abbreviated spelling variants in the ATR outputs for both Latin and French, but never for the edited texts. In at least one instance, the model did not reproduce the masked token exactly but produced the correct reading, since the ATR itself contained an error at that position. For Latin, Top-1 predictions occasionally suggested a different tense or mood of the same verb, which remained grammatically acceptable in context, yet was counted as faulty.

## 6. Conclusion

In this paper, we introduced a large-scale corpus of medieval manuscripts obtained through automatic text recognition, covering a little under 2.5 billion tokens in Latin and Old French. The resource is openly distributed in ALTO, TEI-XML, plain-text and JSON formats enabling research in computational philology, corpus linguistics, NLP, and DH. By providing raw, non-normalized text enriched with structural information, the corpus establishes a new foundation for large-scale experimentation with medieval textual data, from language modelling to the analysis of manuscript layouts.

As future work, we aim to expand the coverage of the corpus both linguistically and geographically. In particular, we plan to integrate Spanish materials from digitized medieval archives, as well as additional Latin and French sources from underrepresented repositories. These extensions will contribute to a broader multilingual resource, enhancing the robustness of NLP models trained on medieval data and opening new perspectives for comparative studies of manuscript culture across Europe. Ultimately, our goal is to establish a scalable, multilingual, and sustainable corpus that will serve the research community for years to come.
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