README.md

---
license: cc-by-4.0
task_categories:
- image-to-text
version: "1.0.0"
language:
- fr
- de
- en
- it
- es
- oc
- la
pretty_name: CATMuS modern (or McCATMuS)
size_categories:
- 100K<n<1M
tags:
- optical-character-recognition
- humanities
- handwritten-text-recognition
- modern documents
- contemporary documents
- good quality
---

![CATMuS Modern Banner](banner_catmus_modern.png)

# Dataset Card for CATMuS Modern and Contemporary (McCATMuS)

Join our Discord to ask questions about the dataset: [![Join the Discord](https://img.shields.io/badge/CATMuS-Discord?style=flat-square&logo=discord&logoColor=%23333333&color=%235865F2)](https://discord.gg/J38xgNEsGk)

## Dataset Details

Handwritten Text Recognition (HTR) has emerged as a crucial tool for converting manuscripts images into machine-readable formats, enabling researchers and scholars to analyze vast collections efficiently. Despite significant technological progress, establishing consistent ground truth across projects for HTR tasks, particularly for complex and heterogeneous historical sources, remains nonetheless challenging.  

We introduce the Consistent Approaches to Transcribing Manuscripts (CATMuS) dataset for **m**odern and **c**ontemporary manuscripts (McCATMuS), which offers:

- a uniform framework framework for annotating modern and contemporary manuscripts;
- a benchmarking environment for evaluating automatic text recognition models across multiple dimensions, enriched with metadata such as century, language, and writing technique;
- a benchmarking environment for additional tasks like script classification and dating approaches;
- a benchmarking environment for exploratory work in computer vision and digital paleography, particularly for line-based tasks including generative approaches.

Built upon datasets from institutions and projects committed to Open Science, McCATMuS provides an interoperable dataset encompassing over 180 manuscripts in 8 different languages. It includes more than 118,000 lines of text and nearly 4 million characters, covering a period from the early 16th century to the present day.

All the datasets were automatically or, when precised, manually corrected to correspond to the CATMuS guidelines, available here: https://catmus-guidelines.github.io/

<!--rephrase: The dataset's consistency in transcription approaches aims to mitigate challenges arising from the diversity in standards for medieval manuscript transcriptions, 
providing a comprehensive benchmark for evaluating HTR models on historical sources. -->

### Dataset Description


- **Curated by:** Alix Chagué
<!--- **Funded by:** <!--BnF Datalab, Biblissima +, DIM PAMIR-->
- **Language(s) (NLP):** French and Middle French, Spanish, Italian, English, Latin, German, Occitan
- **License:** CC-BY 4.0

### Train

| Writing Type | Total Count | Languages and Counts |
|--------------|-------------|----------------------|
| Handwritten | 71296 | French: 65844, Spanish: 2864, German: 1940, English: 390, Italian: 258 |
| Printed | 34684 | French: 30376, Middle French: 1873, Latin: 1592, Italian: 266, Occitan: 258, German: 174, English: 85, Spanish: 60 |
| Typewritten | 298 | English: 298 |

### Validation

| Writing Type | Total Count | Languages and Counts |
|--------------|-------------|----------------------|
| Handwritten | 3833 | French: 3662, Spanish: 149, English: 21, German: 1 |
| Printed | 1825 | French: 1608, Middle French: 115, Latin: 82, Occitan: 12, Spanish: 4, English: 3, German: 1 |
| Typewritten | 18 | English: 18 |

### Test

| Writing Type | Total Count | Languages and Counts |
|--------------|-------------|----------------------|
| Handwritten | 3898 | French: 3724, Spanish: 152, English: 21, German: 1 |
| Printed | 1760 | French: 1546, Middle French: 115, Latin: 82, Occitan: 12, English: 3, Spanish: 1, German: 1 |
| Typewritten | 18 | English: 18 |


## Uses

### Direct Use

- Handwritten Text Recognition
- Date classification
- Script classification

### Out-of-Scope Use

- Text-To-Image

## Dataset Structure

- Data contains the main `split` that can be loaded through `load_dataset("CATMuS/modern")`
- Data can also be split with each manuscript represented in train, val and test using the `gen_split` columns which roughly results in a 90/5/5 split
- The image is in the `im` column, and the text in the `text` column
- Each text line is dated with the combination of `not_before` and `not_after`, the precision of the dating can very greatly depending on the available metadata
- Each text line is associated to a `genre`, a `writing_type` (printed, handwritten or typewritten), a `region_type` and a `line_type` following SegmOnto's vocabulary, a `shelfmark` identifying the documents from which the text line is extracted, and a `project` identifying the project having produced the original dataset.
- When `shelfmark` contains "nobs", it means that the documents are not associated to any known shelfmark, the shelfmark in these cells was thus created for the purpose of this metadataset.

## Annotations

### Annotation process

The annotations in this dataset result:

- for layout extraction, line extraction, typing and transcription, from the original creators of the dataset in most cases, or from automatic or manual corrections by the curator of the CATMuS modern dataset,
- for the rest of the metadata, from automatic or manual collection of the metadata by the curator of the CATMuS modern dataset.

The metadata where set generally set at document level.

The values in`region_type` and `line_type` are, as much as possible, conformant with the [SegmOnto vocabulary](https://segmonto.github.io/).

The values in `writing_type` and `genre` follow a vocabulary set for this dataset:

- possible values in `writing_type` are: `handwritten`, `printed` or `typewritten`.
- possible values in `genre` are: `document of practice`, `drama`, `narratives`, `epistolary`, `treaties`, `poetry`. There can be multiple values, in which case they are separated by semi-colons.

The detail of the annotation rules applied for the transcription of the text can be found at [https://catmus-guidelines.github.io/](https://catmus-guidelines.github.io/).

### Who are the annotators?

This list includes all the annotators identified by the producers of the datasets gathered in McCATMuS dataset. 

*Collecting the names of the authors and annotators of datasets can be combersome. If you think your name was mistakenly added to the list below or if your name is missing, please accept our apologies and do get in touch!*

- Chagué, Alix
- Clérice, Thibault
- Gabay, Simon
- Pinche, Ariane
- Carrow, Jennifer
- Albert, Anaïs
- Bey, Laura
- Champougny, Kevin
- Charbonnier, Pauline
- Chiaretti, Alessandro
- Christensen, Kelly
- Cicchini, Marco
- Clavaud, Florence
- Davoury, Baudoin
- de Champs, Emmanuelle
- Dechavanne, Sylvie
- Denis, Nathalie
- Doat, Soline
- Dubourg Glatigny, Pascal
- Durand, Marc
- Elsa, Falcoz
- Fabert, Eliott
- Faure, Margaux
- Genero, Jean-Damien
- Guimarães, Ingrid
- Humeau, Maxime
- Jacsont, Pauline
  Jahan, Claire
- Jaureguy, Yvan
- Le Fourner, Victoria
- Limon-Bonnet, Marie-Françoise
- Martini, Manuela
- Maurel, Perrine
- Mazoue, Anais
- Meissel, Nina
- Mikhalchuk, Anna
- Nahon, Peter
- Norindr, Jade
- Nougaret, Christine
- Ozturk, Yagmur
- Paupe, Elodie
- Pérez, Gilles
- Rebetez, Jean-Claude
- Riondet, Charles
- Rostaing, Aurélia
- Skilbeck-Gaborit, Eden
- Van Kote, Elsa
- Vanneau, Laurie
- Vlachou-Efstathiou, Malamatenia
- Weddigen, Tristan
- Wojszvzyk, Elise
- ALemoine
- ASJPeronneau
- Alcofrybas
- BeaLct
- CLbt
- Chloelsa
- DMichel
- Desauthieux
- EPerrin
- EdChamps
- GBMireille
- GPINET
- Genea78
- JMGoux
- Jideuxhemme
- LBIsabelle
- Lamotte
- MFGarreau
- MIna
- Maniet
- MarionJo
- PGambette
- PPocard
- PROMBAUT
- PaulineTest
- SCayeux
- SL.
- SLespinasse
- Silver08
- TPellé
- Valérie
- alp
- jmorvan
- lelia
- majubama
- mickael.lefevr
- sgauthier

### Software

The software used to generate this version of the dataset was built by Thibault Clérice and Alix Chagué.

### Reused datasets

All the datasets reused to create the CATMuS Modern and Contemporary dataset are listed below along with the version we used. They can also be found in the [Zotero group](https://www.zotero.org/groups/5601331/catmus_modern__contemporary) dedicated to this metadataset.

- Chagué, A. (2023). *Moonshines* (v2.0.2) [Dataset]. https://github.com/alix-tz/moonshines
- Chagué, A., Champougny, K., Meissel, N., Genero, J.-D., Skilbeck-Gaborit, E., Vanneau, L., Bey, L., Le Fourner, V., Albert, A., Riondet, C., & Martini, M. (2022). *Time Us Corpus* (v0.0.3) [Dataset]. https://doi.org/10.5281/zenodo.6230755 
- Chagué, A., Clérice, T., Mazoue, A., & Van Kote, E. (2024). *CREMMA-AN-TestamentDePoilus* (v1.0.2) [Dataset]. https://doi.org/10.5281/zenodo.10177106
- Chagué, A., Clérice, T., & Van Kote, E. (2023). *CREMMA WIKIPEDIA* (v1.0.4) [Dataset]. https://doi.org/10.5281/zenodo.10666988
- Chagué, A., & Pérez, G. (2023). *Peraire Ground Truth* (v2.1.0) [Dataset]. https://doi.org/10.5281/zenodo.7185907
- Clérice, T. (2021). *CREMMA Early Modern Books* (v0.0.1) [Dataset]. https://doi.org/10.5281/zenodo.5235144
- Clérice, T., Chagué, A., Davoury, B., Doat, S., Faure, M., & Humeau, M. (2022). *CREMMA-MSS-19* (v1.0.0) [Dataset]. HTR United. https://github.com/HTR-United/CREMMA-MSS-19 
- Clérice, T., Chagué, A., Davoury, B., Faure, M., Mazoue, A., & Norindr, J. (2022). *CREMMA-MSS-17* (v1.0.0) [Dataset]. HTR United. https://github.com/HTR-United/CREMMA-MSS-17 
- Clérice, T., Chagué, A., & Doat, S. (2021). *CREMMA-MSS-20* (v1.0.0) [Dataset]. HTR United. https://github.com/HTR-United/CREMMA-MSS-20 (Original work published 2021)
- Gabay, S., Paupe, E., & Rebetez, J.-C. (2024). *FoNDUE-FR-MSS-17* (v1.0.0) [Dataset]. https://github.com/FoNDUE-HTR/FONDUE-FR-MSS-17
- Gabay, S., Nahon, P., Cicchini, M., Jaureguy, Y., & Chappuis, L. (2023). FoNDUE-FR-MSS-18 (Version 1.0) [Data set]. https://github.com/FoNDUE-HTR/FONDUE-FR-MSS-18
- Gabay, S., Pinche, A., Fabert, E., & Christensen, K. (2024). *Imprimés du 18e siècle (Gallicorpora)* (v0.0.17) [Dataset]. https://github.com/Gallicorpora/HTR-imprime-18e-siecle
- Gabay, S., Pinche, A., Fabert, E., Vlachou-Efstathiou, M., Humeau, M., & Christensen, K. (2023). *Imprimés du 17e siècle (Gallicorpora)* (v0.0.46) [Dataset]. https://github.com/Gallicorpora/HTR-imprime-17e-siecle
- Guimarães, I., Maurel, P., Ozturk, Y., Chagué, A., & Clérice, T. (2022). *Memorials for Jane Lathrop Stanford* (v1.0 (corrected)) [Dataset]. https://doi.org/10.5281/zenodo.6126625
- Humeau, M., & Chiaretti, A. (2022). *AraucaniaCorpus* [Dataset]. Araucania Project. https://github.com/Proyecto-Ocupacion-Araucania-UChile/HTR_Araucania_XIX
- Jacsont, P., Simon, G., & Weddigen, T. (2023). *FoNDUE for the Heinrich Wölfflin Fotosammlung of the Kunsthistorisches Institut UZH* (v1.0) [Dataset]. https://github.com/FoNDUE-HTR/FoNDUE_Wolfflin_Fotosammlung
- Jahan, C., & Gabay, S. (2021). *OCR17+—Layout analysis and text recognition for 17th c. French prints* (v1.0) [Dataset]. https://github.com/e-ditiones/OCR17plus
- Limon-Bonnet, M.-F., Chagué, A., & Rostaing, A. (2024). *Notaires de Paris—Bronod (Lectaurep)* (v1.0) [Dataset]. https://doi.org/10.5281/zenodo.10631356 
- Norindr, J., Clérice, T., & Chagué, A. (2023). *HTRomance—Modern* (v0.0.3) [Dataset]. https://github.com/HTRomance-Project/modern-roman-languages
- Rostaing, A., Denis, N., & Chagué, A. (2024). *Notaires de Paris—Mariages et Divorces (Lectaurep)* (v2.0) [Dataset]. https://doi.org/10.5281/zenodo.10632594
- Rostaing, A., Durand, M., & Chagué, A. (2021). *Notaires de Paris—Répertoires (Lectaurep)* (v2.0.0) [Dataset]. https://doi.org/10.5072/zenodo.977691
- Van Kote, E., Faure, M., Norindr, J., Clérice, T., & Chagué, A. (2024). *CREMMA-MSS-18* (v0.0.1) [Dataset]. https://github.com/HTR-United/CREMMA-MSS-18

## Bias, Risks, and Limitations

The data is skewed toward French which is overly represented in the current version of the dataset.

No language is represented over all centuries and all writing type, but French has the better coverage for handwritten text lines.

Only one document is available in Spanish. Occitan is only represented in printed lines.

Since the metadata were set at document level, some lines may incorrectly be set to `handwritten` when they are in fact `printed`, and inversely. Further versions of the dataset will aim to reduce this phenomenon.

## Citation

***TBD***  

Information on the creation process for this dataset can be found in several blog posts: https://alix-tz.github.io/phd/categories/catmus/

<!--- below is the README from CATMuS Medieval --->
<!--
**BibTeX:**

```tex
@unpublished{clerice:hal-04453952,
  TITLE = {{CATMuS Medieval: A multilingual large-scale cross-century dataset in Latin script for handwritten text recognition and beyond}},
  AUTHOR = {Cl{\'e}rice, Thibault and Pinche, Ariane and Vlachou-Efstathiou, Malamatenia and Chagu{\'e}, Alix and Camps, Jean-Baptiste and Gille-Levenson, Matthias and Brisville-Fertin, Olivier and Fischer, Franz and Gervers, Michaels and Boutreux, Agn{\`e}s and Manton, Avery and Gabay, Simon and O'Connor, Patricia and Haverals, Wouter and Kestemont, Mike and Vandyck, Caroline and Kiessling, Benjamin},
  URL = {https://inria.hal.science/hal-04453952},
  NOTE = {working paper or preprint},
  YEAR = {2024},
  MONTH = Feb,
  KEYWORDS = {Historical sources ; medieval manuscripts ; Latin scripts ; benchmarking dataset ; multilingual ; handwritten text recognition},
  PDF = {https://inria.hal.science/hal-04453952/file/ICDAR24___CATMUS_Medieval-1.pdf},
  HAL_ID = {hal-04453952},
  HAL_VERSION = {v1},
}
```

**APA:**

> Thibault Clérice, Ariane Pinche, Malamatenia Vlachou-Efstathiou, Alix Chagué, Jean-Baptiste Camps, et al.. CATMuS Medieval: A multilingual large-scale cross-century dataset in Latin script for handwritten text recognition and beyond. 2024. ⟨hal-04453952⟩
-->

## Dataset Card Contact

Alix Chagué (first.last@inria.fr)