# Transcribing Western modern manuscripts (1500-2020)

Simon Gabay, Tobias Hodel, Ronald Sluijter, Élodie Paupe, Jean-Claude Rebetez, David Rabouin, Vincent Giovannangeli, Walter Boente, Élodie Bascoul, Marion Philip, et al.

► To cite this version:

Simon Gabay, Tobias Hodel, Ronald Sluijter, Élodie Paupe, Jean-Claude Rebetez, et al. Transcribing Western modern manuscripts (1500-2020). DH 2025 - Digital Humanities conference, Alliance of Digital Humanities Organizations (ADHO), Jul 2025, Lisbon, Portugal. hal-05063299

HAL Id: hal-05063299

https://hal.science/hal-05063299v1

Submitted on 12 May 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>
Transcribing Western modern manuscripts (1500-2020):
an economical, ecological and secured approach

Simon Gabay¹, Tobias Hodel², Ronald Sluijter³, Élodie Paupe⁴,
Jean-Claude Rebetez⁴, David Rabouin⁵, Vincent Giovannangeli⁵,

Walter Boente⁶, Élodie Bascoul¹, Marion Philip¹, Marie-Laure

Massot⁷,⁸, Vincent Ventresque⁹,¹⁰, Serena Crespi¹¹, Pauline

Jacsont¹², Yvan Jauregui¹, Loraine Chappuis¹, Esther Solé¹³, Elias

Zimmermann¹,⁶, Maxime Humeau¹⁴, Myriam Lamrayah¹, Justine

Falciola¹, and Alix Chagué¹⁵,¹⁶,¹⁷

¹Université de Genève

{firstname.lastname}@unige.ch

²Universität Bern

{firstname.lastname}@unibe.ch

³Huygens Insitute for the History of the Netherlands

{firstname.lastname}@huygens.knaw.nl

⁴Archives de l'ancien Évêché de Bâle

{firstname.lastname}@aaeb.ch

⁵SPHERE-UMR 7219, C.N.R.S. Paris

{firstname.lastname}@cnrs.fr

⁶Universität Zürich

{firstname.lastname}@uzh.ch

⁷CAPHÉS-UAR 3610, C.N.R.S. Paris

⁸École normale supérieure de Paris | PSL

{firstname.lastname}@ens.ch

⁹TRIANGLE-UMR 5206, C.N.R.S. Lyon

¹⁰École normale supérieure de Lyon

{firstname.lastname}@ens-lyon.fr

¹¹Université de Tours

{firstname.lastname}@univ-tours.fr

¹²Swiss Academy of Humanities and Social Sciences

{firstname.lastname}@unibas.ch

¹³Universitat de Lleida

{firstname.lastname}@udl.cat

¹⁴Université de Lausanne

{firstname.lastname}@unil.ch

¹⁵Inria Paris

{firstname.lastname}@inria.fr

¹⁶Université de Montréal

¹⁷École Pratique des Hautes Études, Paris
Research in the Humanities requires large datasets that often exist only in analogue format. Extracting textual information from manuscripts and printed books is therefore a crucial step for the future of computational humanities, as has been shown by recent works in history (Gordijn 2023), historical linguistics (Gabay and Clérice 2024), etc.

Since OCRopy (Breuel 2014), several Optical Character Recognition (OCR) engines have been developed to extract the text from digital facsimiles, such as PyLaia (Puigcerver 2017; Tarride et al. 2024), Kraken (Kiessling 2019) or Calamari (Wick, Reul, and Puppe 2020) – to name just a few. Several all-in-one platforms have been built around these OCR engines, simplifying the user experience to annotate and train models for layout analysis, OCR, reading order, etc. such as Transkribus (Colutto et al. 2019; Kahle et al. 2017), eScriptorium (Kiessling et al. 2019; Stokes et al. 2021) or OCR4all (Reul et al. 2019).

One of the main problems with OCR engines is the cost of the infrastructure required for training models, which leaves two possibilities. On the one hand, one can become a paid (and therefore closed) solution to continue providing the necessary computing power; on the other hand, one can remain a completely open software, but leave it up to researchers to buy and manage their own hardware.

Both options have their respective merits, but we want to draw attention here to two positive consequences of an open solution, despite the technical complexities it can potentially generate. First, it remains possible to deploy a lightweight version (i.e. without a training option) of open tools on any machine for institutions and researchers who cannot afford a paid one (Salar Khan, Umer, and Faruqe 2024). Second, an open-source solution allows IT security teams to control the code and run it on premises in a completely sealed environment – something that is absolutely necessary for highly sensitive data like legal or medical documents (Herzog, Celik, and Sulaiman 2024).

In this paper, we evaluate the possibility of creating a diachronic multilingual model for Western European languages. This massive model is produced under complex conditions, by aggregating data from various projects that have different transcription guidelines. Despite this, it allows for producing reasonable results from scratch, or to be fine-tuned on CPUs only, in case researchers do not have access to GPUs for financial, security, or ecological reasons (Pendergrass et al. 2019).

# 1 Massive models, complex data

We know that multilingual models offer promising results (Capurro, Provatorova, and Kanoulas 2023). Transkribus has released in 2023 the “Titan supermodel” (Trankribus Team 2023), a multilingual transformer-based model whose efficiency is proven to be excellent with complicated historical documents such as postcards in different languages (Smits et al. 2023), even if it now seems challenged by multimodal Large Language Models (LLMs, Humphries et al. 2024). LLMs are based on a similar approach (massive and multilingual models), but because they were (still) paid solutions and did not (yet) work in a sealed environment when we began this study, they were excluded.

Open alternatives have recently emerged, notably for medieval manuscripts (Clérice et al. 2024; Pinche et al. 2024), or prints with Gothic (Solfrini, Gabay, Humeau, et al. 2024) or Latin (Gabay, Clérice, et al. 2024) characters. In addition to being open, these models also have the particularity of being based on homogeneous and documented transcription guidelines, prepared by experts in philology (e.g. Pinche 2022; Solfrini, Gabay, Gross, et al. 2023), coordinated by the CATMuS project (Pinche et al. 2024).
<figure><img src="image_2.png" /><figcaption>Figure 1: Archives de l'ancien Évêché de Bâle, Porrentruy, Switzerland, B 168/14-17.1 (1589), f° 1r.</figcaption></figure>

Initial tests have been conducted for French modern manuscripts with encouraging, but not yet satisfactory, results, whether it is for the Manu McFrench model (Chagué, Clérice, et al. 2023) or the Manu McFondue model (Gabay, Pinche, et al. 2024). One of the main problems, on top of the extreme diversity of the hands, is the existence of many datasets prepared without coordinated transcription guidelines. The development of abbreviations, the transcription of allographs, etc. is therefore different from one project to another (cf. fig. 1 and ex. 1), which considerably complicates the training task.

Depuis les gens font tousjours venus vers elle de tout pays, fut pour choses perdues, ou pour garir gens et bestes. mais de elles mesme ne leurs pouuoit aider que premierement

Depuis les gens sont tousjours venus vers elle de tout pays, fut pour choses perdues, ou pour garir gens et bestes. mais de elles mesme ne leurs pouoit aider que premierement

Example 1: Two possible transcriptions of fig. 1, with an abbreviation in orange, variations of s in blue, and a ramist letter in red.

## 2 Experiment

**Data** Gathering data is not trivial: although many projects have their datasets referenced on HTR-United (Chagué and Clérice 2023), it is not the case for all of them (copyright issues, lack of knowledge of the project, etc.). All data used for our experiment is listed in the tab. 1, the bulk of which is written in Dutch, German and French – the datasets for Latin and Spanish are of much smaller magnitude.

As for diversity, the small number of projects in Dutch hides a great diversity of documents, carefully selected by specialists. The data in German are in large quantity, but less diversified in their graphic form, while those in French are more diversified, but to the detriment of the homogeneity of transcription standards.
<table><caption>Table 1: Data used for training.</caption><thead><tr><th>Project</th><th>Language</th><th>Century</th><th>Pages</th><th>Lines</th><th>available</th></tr></thead><tbody><tr><td>IJSBERG</td><td>nl</td><td>17th-18th c.</td><td>6 275</td><td>259 517</td><td>Yes</td></tr><tr><td>REPUBLIC</td><td>nl</td><td>17th c.</td><td>514</td><td>46 690</td><td>Yes</td></tr><tr><td>Subtotal</td><td></td><td></td><td>6 789</td><td>306 207</td><td></td></tr><tr><td>Heinrich Wölfflin</td><td>de/it</td><td>20th c.</td><td>1 107</td><td>2 290</td><td>Yes</td></tr><tr><td>EditionEugenHuber</td><td>de</td><td>20th c.</td><td>1 921</td><td>45 194</td><td>No</td></tr><tr><td>Swiss Federal Council</td><td>de</td><td>19th c.</td><td>2 752</td><td>2 752</td><td>Yes</td></tr><tr><td>Staatsarchivs Zürich</td><td>de</td><td>19th c.</td><td>2 969</td><td>94 201</td><td>No</td></tr><tr><td>Meyer von Schauensee</td><td>de</td><td>18th c.</td><td>242</td><td>9 303</td><td>Yes</td></tr><tr><td>Crimes et châtiments</td><td>de</td><td>17th c.</td><td>95</td><td>1 756</td><td>Yes</td></tr><tr><td>Basler Urfehdebuchs X</td><td>de</td><td>16th c.</td><td>192</td><td>8 221</td><td>Yes</td></tr><tr><td>Crimes et châtiments</td><td>de</td><td>16th c.</td><td>65</td><td>1 269</td><td>Yes</td></tr><tr><td>Subtotal</td><td></td><td></td><td>9 343</td><td>164 986</td><td></td></tr><tr><td>Ricardo Viñes</td><td>sp</td><td>20th c.</td><td>48</td><td>2 168</td><td>Soon</td></tr><tr><td>Occupation of Araucania</td><td>sp</td><td>19th c.</td><td>146</td><td>3 235</td><td>Partially</td></tr><tr><td>Subtotal</td><td></td><td></td><td>194</td><td>5 403</td><td></td></tr><tr><td>Philiumm</td><td>la</td><td>17th c.</td><td>207</td><td>13 049</td><td>Partially</td></tr><tr><td>Sardi's Toponomasia</td><td>la</td><td>16th c.</td><td>51</td><td>2 003</td><td>Partially</td></tr><tr><td>Subtotal</td><td></td><td></td><td>258</td><td>15 052</td><td></td></tr><tr><td>Moonshines</td><td>fr</td><td>21th c.</td><td>54</td><td>1 186</td><td>Yes</td></tr><tr><td>CREMMA-wikipedia</td><td>fr</td><td>21th c.</td><td>357</td><td>1 973</td><td>Yes</td></tr><tr><td>CREMMA-MSS-20</td><td>fr</td><td>20th c.</td><td>15</td><td>224</td><td>Yes</td></tr><tr><td>Digital Peraire</td><td>fr</td><td>20th c.</td><td>75</td><td>2 569</td><td>Yes</td></tr><tr><td>Testaments de Poilus</td><td>fr</td><td>20th c.</td><td>228</td><td>3 330</td><td>Yes</td></tr><tr><td>Verbier Time Machine</td><td>fr</td><td>19th-20th c.</td><td>192</td><td>28 476</td><td>Partially</td></tr><tr><td>Lectaurep (répertoires)</td><td>fr</td><td>19th-20th c.</td><td>223</td><td>29 411</td><td>Yes</td></tr><tr><td>FoNDUE-FR-MSS-19</td><td>fr</td><td>19th c.</td><td>67</td><td>1 540</td><td>Yes</td></tr><tr><td>Timeus</td><td>fr</td><td>19th c.</td><td>250</td><td>7 701</td><td>Yes</td></tr><tr><td>Lectaurep (Mariages)</td><td>fr</td><td>19th c.</td><td>107</td><td>20 304</td><td>Yes</td></tr><tr><td>Private archives</td><td>fr</td><td>19th c.</td><td>82</td><td>2 235</td><td>No</td></tr><tr><td>Proto4DigEd</td><td>fr</td><td>19th c.</td><td>107</td><td>1 400</td><td>No</td></tr><tr><td>CREMMA-MSS-19</td><td>fr</td><td>19th c.</td><td>79</td><td>1 807</td><td>Yes</td></tr><tr><td>FoNDUE-FR-MSS-18</td><td>fr</td><td>18th c.</td><td>333</td><td>8 496</td><td>Yes</td></tr><tr><td>Masculinités esclavagistes</td><td>fr</td><td>18th c.</td><td>282</td><td>7 566</td><td>Yes</td></tr><tr><td>CREMMA-MSS-18</td><td>fr</td><td>18th c.</td><td>136</td><td>4 019</td><td>Yes</td></tr><tr><td>Lectaurep (Bronod)</td><td>fr</td><td>18th c.</td><td>101</td><td>3 707</td><td>Yes</td></tr><tr><td>FoNDUE-FR-MSS-17</td><td>fr</td><td>17th c.</td><td>8</td><td>91</td><td>Yes</td></tr><tr><td>CREMMA-MSS-17</td><td>fr</td><td>17th c.</td><td>115</td><td>2 213</td><td>Yes</td></tr><tr><td>Crimes et châtiments</td><td>fr</td><td>17th c.</td><td>882</td><td>22 359</td><td>Yes</td></tr><tr><td>HTRomance</td><td>fr</td><td>16th-19th c.</td><td>173</td><td>3 385</td><td>Yes</td></tr><tr><td>Crimes et châtiments</td><td>fr</td><td>16th c.</td><td>500</td><td>14 856</td><td>Yes</td></tr><tr><td>Subtotal</td><td></td><td></td><td>4 177</td><td>168 848</td><td></td></tr><tr><td>Total</td><td></td><td></td><td>20 761</td><td>660 496</td><td></td></tr></tbody></table>
**Preparation** When needed, the data in PageXML are transformed into an ALTO format. For documents from Transkribus, the height of the baseline is controlled and corrected if it is too low (a few pixels less vertically). The masks are all repolygonised with Kraken 5.2, and the characters are harmonised with Chocomufin (e.g. straight vs curly quotes).

**Training** The model is trained with Kraken. We use a batch of 70, a learning rate of $\approx \sqrt{70} \cdot 10^{-4}$ (i.e. $8e^{-4}$), following Kraken's recommendations, an automatic mixed precision, a NFD (Normalization Form canonical Decomposition) Unicode normalisation and data augmentation. Rather than XML files, we use precompiled binary dataset files. The model produced is called FoNDUE-GD and its training lasted 30 epochs.

In a second step, we produce a model per linguistic domain by fine-tuning FoNDUE-GD only on data from the same language, still with the same configuration, except the batch size (16) and the learning rate ($4e^{-4}$).

<table><caption>Table 2: Accuracy for each model on generic and specific test sets. ManuMcFondue serves as a baseline. FoNDUE-GD<sub>ft</sub> are versions of FoNDUE-GD fine-tuned on all the data in the same language that those tested. Best scores are in bold.</caption><thead><tr><th>Test set</th><th>Language</th><th>Century</th><th>ManuMcFondue</th><th>FoNDUE-GD</th><th>FoNDUE-GD<sub>ft</sub></th></tr></thead><tbody><tr><td>Test 1 (random selection)</td><td>multi</td><td>16th-21th c.</td><td>73.54</td><td><strong>89.05</strong></td><td></td></tr><tr><td>Test 2 (curated selection)</td><td>multi</td><td>16th-21th c.</td><td>76.43</td><td><strong>85.30</strong></td><td></td></tr><tr><td>REPUBLIC</td><td>nl</td><td>17th c.</td><td>85.00</td><td>96.36</td><td><strong>97.13</strong></td></tr><tr><td>IJSBERG</td><td>nl</td><td>18th c.</td><td>89.09</td><td>93.13</td><td><strong>95.01</strong></td></tr><tr><td>IJSBERG</td><td>nl</td><td>19th c.</td><td>94.60</td><td>93.60</td><td><strong>95.04</strong></td></tr><tr><td>IJSBERG</td><td>nl</td><td>20th c.</td><td>93.41</td><td>91.41</td><td><strong>94.17</strong></td></tr><tr><td>EditionEugenHuber</td><td>de</td><td>20th c.</td><td>44.81</td><td>90.72</td><td><strong>93.20</strong></td></tr><tr><td>Staatsarchiv Zurich 1</td><td>de</td><td>19th c.</td><td>66.02</td><td>92.08</td><td><strong>93.31</strong></td></tr><tr><td>Staatsarchiv Zurich 2</td><td>de</td><td>19th c.</td><td>54.24</td><td>94.14</td><td><strong>94.97</strong></td></tr><tr><td>Meyer von Schauensee</td><td>de</td><td>18th c.</td><td>70.35</td><td>87.45</td><td><strong>88.31</strong></td></tr><tr><td>Crimes et châtiments</td><td>de</td><td>16th-17th c.</td><td>55.81</td><td>75.22</td><td><strong>80.17</strong></td></tr><tr><td>Basler Urfehdebuchs X</td><td>de</td><td>16th c.</td><td><strong>78.98</strong></td><td>22.09</td><td>43.27</td></tr><tr><td>Ricardo Viñes</td><td>sp</td><td>20th c.</td><td>52.00</td><td>74.53</td><td><strong>84.57</strong></td></tr><tr><td>Occupation of Araucania</td><td>sp</td><td>19th</td><td>80.87</td><td>82.59</td><td><strong>88.69</strong></td></tr><tr><td>Philiumm</td><td>la</td><td>17th c.</td><td>68.46</td><td>90.78</td><td><strong>94.86</strong></td></tr><tr><td>Sardi's Toponomasia</td><td>la</td><td>16th c.</td><td>88.14</td><td>88.27</td><td><strong>92.86</strong></td></tr><tr><td>Moonshines</td><td>fr</td><td>21th c.</td><td>67.84</td><td>84.38</td><td><strong>89.26</strong></td></tr><tr><td>CREMMA-wikipedia</td><td>fr</td><td>21th c.</td><td>69.09</td><td>79.47</td><td><strong>83.42</strong></td></tr><tr><td>Testaments de Poilus</td><td>fr</td><td>20th c.</td><td>71.77</td><td>77.56</td><td><strong>81.13</strong></td></tr><tr><td>Digital Peraire</td><td>fr</td><td>20th c.</td><td>71.12</td><td>82.32</td><td><strong>86.63</strong></td></tr><tr><td>Verbier Time Machine</td><td>fr</td><td>19th-20th c.</td><td>85.44</td><td>83.68</td><td><strong>87.82</strong></td></tr><tr><td>Lectairep (répertoires) 1</td><td>fr</td><td>19th-20th c.</td><td><strong>92.03</strong></td><td>86.73</td><td>90.52</td></tr><tr><td>Lectairep (répertoires) 2</td><td>fr</td><td>19th-20th c.</td><td><strong>86.63</strong></td><td>83.19</td><td>86.51</td></tr><tr><td>FoNDUE-FR-MSS-19 (Zola)</td><td>fr</td><td>19th c.</td><td>78.24</td><td>79.32</td><td><strong>84.34</strong></td></tr><tr><td>Timeus</td><td>fr</td><td>19th c.</td><td>60.52</td><td>83.61</td><td><strong>87.23</strong></td></tr><tr><td>Lectairep (mariages)</td><td>fr</td><td>19th-20th c.</td><td>89.38</td><td>88.64</td><td><strong>90.30</strong></td></tr><tr><td>Private archives</td><td>fr</td><td>19th c.</td><td>86.88</td><td>82.73</td><td><strong>87.03</strong></td></tr><tr><td>Proto4DigEd</td><td>fr</td><td>19th c.</td><td>51.54</td><td>70.71</td><td><strong>79.98</strong></td></tr><tr><td>FoNDUE-MSS-18 (Bastille)</td><td>fr</td><td>18th c.</td><td>78.47</td><td>86.58</td><td><strong>90.74</strong></td></tr><tr><td>FoNDUE-MSS-18 (R. Simon)</td><td>fr</td><td>18th c.</td><td>92.77</td><td>92.24</td><td><strong>93.22</strong></td></tr><tr><td>HTRomance</td><td>fr</td><td>17th-18th c.</td><td>80.08</td><td>86.71</td><td><strong>89.14</strong></td></tr><tr><td>Crimes et châtiments</td><td>fr</td><td>16th-17th c.</td><td>63.49</td><td>82.98</td><td><strong>88.00</strong></td></tr></tbody></table>
## 2.1 In-domain testing

Testing the effectiveness of the model is not trivial, because the corpus is heavily imbalanced (up to 1:35 for the ratio between pages in Latin and in Spanish). To skirt this problem, two test sets are prepared.

* The first one is a random selection of 2% of the pages (i.e. c. 420 pages, cf. Test 1 in tab. 2), which offers an evaluation of the model relative to the training data. In practice: there are a lot of data in Dutch, much less in Latin.

* The second one uses a curated selection of 4 pages taken from different projects. By doing so we increase the representation of scarce classes: there are, relatively to the corpus, more data in Latin, and the ratio between data in Latin and in Dutch is now of 1:2 in the test set. Therefore, we make possible an evaluation on partially rebalanced data (cf. Test 2 in tab. 2), but also the execution of separate tests for each class for a more detailed study of the performance of the model.

The results are extremely satisfactory, with an accuracy of c. 85-90% for almost all languages, and all periods. Some projects that have not seen the amount of data increase between the training of Manu McFondue see their prediction improve only thanks to the addition of new data, and the heterogeneity of the transcription practices seems to have a minimal impact.

The idea of fine-tuning specific models for each linguistic domain from the massive model seems to be a profitable strategy, and significantly improves the quality of the prediction. The languages least well represented in the train set of the massive model are logically those which are experiencing the strongest increase with linguistic fine-tuning, certainly because it counterbalances the small quantity of data used when training the massive model and because the data are less diverse.

## 3 Experiment 2

<figure><img src="image_3.png" /></figure>

A la moelle cervicale apparamment
pas d'atrophie des cordons post
Moelle cervicale
melanose meningée
nervures des cornes antérieures. SP.
légère paleur des faisseaux latéraux avec
leger gonflement axonal.
CA PS+ Alz-
FEm PS-
FA PS (+)
PO PS +.

<caption>Figure 2: Example of a medical document. We observe several hands, several pens, slightly erased areas, etc.</caption>

In order to more precisely measure the efficiency of such secondary models, we propose another experiment: we test FoNDUE-GD on out-of-domain (OOD) data. We choose two new datasets:
* Italian data, a language (almost) unknown to FoNDUE-GD. This dataset is made of manuscripts from the 18th century written by historian Ludovico Antonio Muratori (1672-1750) (Cascianelli et al. 2022).

* Medical data from the 20th century, the form and lexical content of which also diverge strongly from those of the massive model (cf. fig. 2).

Fine-tuning is done with only one CPU. We try to fine-tune the generic model, FoNDUE-GD, and its fine-tuned version on French data, $FoNDUE-GD^{fr}$, because Italian is also a Romance language and the medical data are in French.

<table><caption>Table 3: Out-of-domain test sets with fine-tuning scores. FoNDUE-GD is the baseline, $FoNDUE-GD_{ft}$ is the fine-tuned version of FoNDUE-GD on the OOD data, $FoNDUE-GD_{ft}^{fr}$ is the fine-tuned version of FoNDUE-GD on the OOD data.</caption><thead><tr><th>Type</th><th>Language</th><th>Century</th><th>Pages (test)</th><th>Lines (Test)</th><th>FoNDUE-GD</th><th>FoNDUE-GD<sub>ft</sub></th><th>FoNDUE-GD<sub>ft</sub><sup>fr</sup></th></tr></thead><tbody><tr><td>Correspondance</td><td>it</td><td>18th</td><td>15 (2)</td><td>334 (37)</td><td>80.68</td><td>86.06</td><td><strong>88.51</strong></td></tr><tr><td>Medical records</td><td>fr</td><td>20th</td><td>156 (5)</td><td>2427 (41)</td><td>68.35</td><td>75.93</td><td><strong>77.37</strong></td></tr></tbody></table>

Once again, the results are extremely satisfactory, with an accuracy of slightly less than 90% with 13 pages in Italian, and almost 80% for extremely complicated medical data.

# 4 Conclusion

FoNDUE-GD should not (only) be seen as a mega model, but as a primary model from which secondary models dedicated to specific tasks are derived. While our model can be used out of the box for many documents, it can also be fine-tuned with limited data to be adapted to specific scenarios, such as prediction in a new language or for complex documents.

Given the impossibility of correcting all the transcriptions contained in our dataset, the question of data harmonization is the next challenge before us. One solution would be to evaluate how quickly our massive model can “unlearn” the conflicting rules it has seen by fine-tuning it with a homogeneous training set.

# Acknowledgments

Thank you to Th. Clérice for finding the name of the model (among many things), A. Pinche for her comments on the text and the experiments.

# Funding

This paper has been funded by the TextEnt (FNS N°220833), *Masculinités esclavagistes* (FNS N°219753), Archimed (FNS N°219457), Philiumm (ERC advanced Grant N°101020985) and Prima (ERC advanced Grant N°101142242) projects.
References

Breuel, Thomas M. (2014). *Ocropy: Python-based tools for document analysis and OCR*. URL: https://github.com/tmbdev/ocropy.

Capurro, Carlotta, Vera Provatorova, and Evangelos Kanoulas (2023). "Experi-
menting with Training a Neural Network in Transkribus to Recognise Text in a Multilin-
gual and Multi-Authored Manuscript Collection". In: *Heritage* 6.12, pp. 7482–7494. DOI:
10.3390/heritage6120392.

Cascianelli, Silvia, Vittorio Pippi, Martin Maarand, Marcella Cornia, Lorenzo Baraldi, Christopher Kermorvant, and Rita Cucchiara (2022). "The LAM Dataset: A Novel Benchmark for Line-Level Handwritten Text Recognition". In: *2022 26th International Conference on Pattern Recognition (ICPR)*. Montreal, Canada, pp. 1506–1513. DOI: 10.1109/ICPR56361.2022.9956189.

Chagué, Alix and Thibault Clérice (2023). "I'm here to fight for ground truth: HTR-United, a solution towards a common for HTR training data". In: *Digital Humanities 2023: Collaboration as Opportunity*. Alliance of Digital Humanities Organizations. Graz, Austria. URL: https://inria.hal.science/hal-04094233.

Chagué, Alix, Thibault Clérice, Jade Norindr, Maxime Humeau, Baudoin Davoury, Elsa Van Kote, Anaïs Mazoue, Margaux Faure, and Soline Doat (2023). "Manu McFrench, from zero to hero: impact of using a generic handwriting recognition model for smaller datasets". In: *Digital Humanities 2023: Collaboration as Opportunity*. Alliance of Digital Humanities Organizations. Graz, Austria. URL: https://inria.hal.science/hal-04094241.

Clérice, Thibault, Ariane Pinche, Malamatenia Vlachou-Efstathiou, Alix Chagué, Jean-Baptiste Camps, Matthias Gille-Levenson, Olivier Brisville-Fertin, Franz Fischer, Michaels Gervers, Agnès Boutreux, Avery Manton, Simon Gabay, Patricia O'Connor, Wouter Haverals, Mike Kestemont, Caroline Vandyck, and Benjamin Kiessling (2024). "CATMuS Medieval: A multilingual large-scale cross-century dataset in Latin script for handwritten text recognition and beyond". In: *2024 International Conference on Document Analysis and Recognition (ICDAR)*. Athens, Greece. URL: https://inria.hal.science/hal-04453952.

Colutto, Sebastian, Philip Kahle, Hackl Guenter, and Guenter Muehlberger (2019). "Transkribus. A Platform for Automated Text Recognition and Searching of Historical Documents". In: *2019 15th International Conference on eScience (eScience)*. San Diego, CA, USA, pp. 463–466. DOI: 10.1109/eScience.2019.00060.

Gabay, Simon and Thibault Clérice (2024). "The birth of French orthography. A computational analysis of French spelling systems in diachrony". In: *Proceedings of the Computational Humanities Research Conference 2024*. Aarhus, Danemark: CEUR Workshop Proceedings, pp. 246–264. URL: https://ceur-ws.org/Vol-3834/paper21.pdf.

Gabay, Simon, Thibault Clérice, Pauline Jacsont, Elina Leblanc, Marie Jeannot-Tirole, Sonia Solfrini, Sophie Dolto, Floriane Goy, Carmen Carrasco Luján, Maddalena Zaglio, Myriam Perregaux, Juliette Janes, Benoît Sagot, Rachel Bawden, Rasul Dent, Oriane Nédey, and Alix Chagué (2024). "Reconnaissance des écritures dans les imprimés". In: *Humanistica 2024*. OCR. Association francophone des humanités numériques. Meknes, Morocco. URL: https://hal.science/hal-04557457.

Gabay, Simon, Ariane Pinche, Peter Nahon, Alix Chagué, Pauline Jacsont, Élodie Paupe, Jean-Claude Rebetez, Maxime Humeau, Christine Payot,
Thibault Maillard, Yvan Jauregui, Elina Leblanc, and Loraine Chappuis (2024). "Vers un modèle diachronique pour les mains modernes françaises". In: *Humanistica 2024. Association francophone des humanités numériques. Meknes, Marocco*. URL: https://hal.science/hal-04801645.

Gordijn, Femke (2023). "Reasoning with Turbulence: A Quantitative Analysis of Extraordinary Times in the Digitised Resolutions of the Dutch States-General, 1705-1796". In: *Early Modern Low Countries* 7.2, pp. 131–153. DOI: 10.51750/emlc18373.

Herzog, Nitsa J., Dilek Celik, and Rejwan Bin Sulaiman (2024). “Artificial Intelligence in Healthcare and Medical Records Security”. In: *Cybersecurity and Artificial Intelligence: Transformational Strategies and Disruptive Innovation*. Ed. by Hamid Jahankhani, Gordon Bowen, Mhd Saeed Sharif, and Osama Hussien. Cham: Springer Nature Switzerland, pp. 35–57. DOI: 10.1007/978-3-031-52272-7_2.

Humphries, Mark, Lianne C. Leddy, Quinn Downton, Meredith Legace, John McConnell, Isabella Murray, and Elizabeth Spence (2024). *Unlocking the Archives: Using Large Language Models to Transcribe Handwritten Historical Documents*. URL: https://arxiv.org/abs/2411.03340.

Kahle, Philip, Sebastian Colutto, Günter Hackl, and Günter Mühlberger (2017). "Transkribus - A Service Platform for Transcription, Recognition and Retrieval of Historical Documents". In: *2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)*. Vol. 04. Kyoto, Japan, pp. 19–24. DOI: 10.1109/ICDAR.2017.307.

Kiessling, Benjamin (2019). "Kraken - a Universal Text Recognizer for the Humanities". In: *Digital Humanities 2019 Conference papers*. Alliance of Digital Humanities Organizations. Utrecht, The Netherlands. DOI: 10.34894/Z9G2EX.

Kiessling, Benjamin, Robin Tissot, Peter Stokes, and Daniel Stökl Ben Ezra (2019). "eScriptorium: An Open Source Platform for Historical Document Analysis". In: *2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)*. Vol. 2. Sydney, Australia, pp. 19–19. DOI: 10.1109/ICDARW.2019.10032.

Pendergrass, Keith L., Walker Sampson, Tessa Walsh, and Laura Alagna (2019). "Toward Environmentally Sustainable Digital Preservation". In: *The American Archivist* 82 (1), pp. 165–206. DOI: 10.17723/0360-9081-82.1.165.

Pinche, Ariane (2022). "Guide de transcription pour les manuscrits du Xe au XVe siècle". working paper. URL: https://hal.science/hal-03697382.

Pinche, Ariane, Thibault Clérice, Alix Chagué, Jean-Baptiste Camps, Malamatenia Vlachou-Efstathiou, Matthias Gille Levenson, Olivier Brisville-Fertin, Federico Boschetti, Franz Fischer, Michael Gervers, Agnès Boutreux, Avery Manton, Simon Gabay, Wouter Haverals, Mike Kestemont, Caroline Vandyck, and Patricia O’Connor (2024). "CATMuS-Medieval: Consistent Approaches to Transcribing ManuScripts". In: *Digital Humanities - DH2024*. Alliance of Digital Humanities Organizations. Washington, D.C., United States. URL: https://inria.hal.science/hal-04346939.

Puigcerver, Joan (2017). "Are multidimensional recurrent layers really necessary for handwritten text recognition?" In: *2017 14th IAPR international conference on document analysis and recognition (ICDAR)*. Vol. 1. IEEE. Kyoto, Japan, pp. 67–72. DOI: 10.1109/ICDAR.2017.20.

Reul, Christian, Dennis Christ, Alexander Hartelt, Nico Balbach, Maximilian Wehner, Uwe Springmann, Christoph Wick, Christine Grundig, Andreas Büttner, and Frank Puppe (2019). "OCR4all—An Open-Source Tool Providing a
(Semi-)Automatic OCR Workflow for Historical Printings". In: *Applied Sciences* 9.22. DOI: 10.3390/app9224853.

Salar Khan, Muhammad, Hamza Umer, and Farhana Faruqe (2024). "Artificial intelligence for low income countries". In: *Humanities and Social Sciences Communications* 11 (1422). DOI: 10.1057/s41599-024-03947-w.

Smits, Thomas, Wouter Haverals, Loren Verreyen, Mona Allaert, and Mike Kestemont (2023). "Greetings from! Extracting address information from 100,000 historical picture postcards". In: *Proceedings of the Computational Humanities Research Conference 2023*. Paris, France: CEUR Workshop Proceedings, pp. 512–529. URL: https://ceur-ws.org/Vol-3558/paper6180.pdf.

Solfrini, Sonia, Simon Gabay, Geneviève Gross, Pierre-Olivier Beaulnes, Aurélia M Oliveira, and Daniela Solfaroli Camillocci (2023). "Guide de transcription pour les imprimés français du XVIe siècle en caractères gothiques". working paper. URL: https://hal.science/hal-04281804.

Solfrini, Sonia, Simon Gabay, Maxime Humeau, Ariane Pinche, Pierre-Olivier Beaulnes, Aurélia Marques Oliveira, Geneviève Gross, and Daniela Solfaroli Camillocci (2024). "Océriser les imprimés du XVIe siècle en langue française". In: *Humanistica 2024*. OCR. Association francophone des humanités numériques. Meknes, Morocco. URL: https://hal.science/hal-04555002.

Stokes, Peter Anthony, Benjamin Kiessling, Daniel Stökl Ben Ezra, Robin Tissot, and El Hassane Gargem (2021). "The eScriptorium VRE for Manuscript Cultures". In: *Classics*@ 18.1. URL: https://ens.hal.science/hal-03991423.

Tarride, Solène, Yoann Schneider, Marie Generali-Lince, Mélodie Boillet, Bastien Abadie, and Christopher Kermorvant (2024). "Improving Automatic Text Recognition with Language Models in the PyLaia Open-Source Library". In: *Document Analysis and Recognition - ICDAR 2024*. Ed. by Elisa H. Barney Smith, Marcus Liwicki, and Liangrui Peng. Cham: Springer Nature Switzerland, pp. 387–404. DOI: 10.1007/978-3-031-70549-6_23.

Trankribus Team (2023). *Introducing Transkribus Super Models - Get Access to 'The Text Titan I'.* URL: https://readcoop.eu/introducing-transkribus-super-models-get-access-to-the-text-titan-i.

Wick, Christoph, Christian Reul, and Frank Puppe (2020). "Calamari – A High-Performance Tensorflow-based Deep Learning Package for Optical Character Recognition". In: *Digital Humanities Quarterly* 14 (2). URL: https://www.digitalhumanities.org/dhq/vol/14/2/000451/000451.html.