metadata

license: cc-by-4.0
task_categories:
  - image-to-text
language:
  - fr
  - en
  - nl
  - it
  - es
  - ca
pretty_name: CATMuS Medieval
size_categories:
  - 100K<n<1M
tags:
  - optical-character-recognition
  - humanities
  - handwritten-text-recognition

Dataset Card for CATMuS Medieval

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 analyse vast collections efficiently. Despite significant technological progress, establishing consistent ground truth across projects for HTR tasks, particularly for complex and heterogeneous historical sources like medieval manuscripts in Latin scripts (8th-15th century CE), remains nonetheless challenging. We introduce the Consistent Approaches to Transcribing Manuscripts (CATMuS) dataset for medieval manuscripts, which offers:

a uniform framework for annotation practices for medieval manuscripts,
a benchmarking environment for evaluating automatic text recognition models across multiple dimensions thanks to rich metadata (century of production, language, genre, script, etc.),
a benchmarking environment for other tasks (such as script classification or dating approaches),
a benchmarking environment and finally for exploratory work pertaining to computer vision and digital paleography around line-based tasks, such as generative approaches.

Developed through collaboration among various institutions and projects, CATMuS provides an inter-compatible dataset spanning more than 200 manuscripts and incunabula in 10 different languages, comprising over 160,000 lines of text and 5 million characters spanning from the 8th century to the 16th.

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: Thibault Clérice
Funded by: BnF Datalab, Biblissima +, DIM PAMIR
Language(s) (NLP): Middle and Old French, Middle Dutch, Catalan, Spanish, Navarese, Italian, Venitian, Old English, Latin
License: CC-BY

Uses

Direct Use

Handwritten Text Recognition
Data classification
Script classification

Out-of-Scope Use

Text-To-Image

Dataset Structure

Data contains the main split that is loaded through load_dataset("CATMuS/medieval")
Data can be split with each manuscript inside train, val and test using the gen_split columns which results in a 90/5/5 split
The image is in the im column, and the text in the text column

Annotations [optional]

Annotation process

The annotation process is described in the dataset paper.

Who are the annotators?

Pinche, Ariane
Clérice, Thibault
Chagué, Alix
Camps, Jean-Baptiste
Vlachou-Efstathiou, Malamatenia
Gille Levenson, Matthias
Brisville-Fertin, Olivier
Boschetti, Federico
Fischer, Franz
Gervers, Michael
Boutreux, Agnès
Manton, Avery
Gabay, Simon
Bordier, Julie
Glaise, Anthony
Alba, Rachele
Rubin, Giorgia
White, Nick
Karaisl, Antonia
Leroy, Noé
Maulu, Marco
Biay, Sébastien
Cappe, Zoé
Konstantinova, Kristina
Boby, Victor
Christensen, Kelly
Pierreville, Corinne
Aruta, Davide
Lenzi, Martina
Le Huëron, Armelle
Possamaï, Marylène
Duval, Frédéric
Mariotti, Violetta
Morreale, Laura
Nolibois, Alice
Foehr-Janssens, Yasmina
Deleville, Prunelle
Carnaille, Camille
Lecomte, Sophie
Meylan, Aminoel
Ventura, Simone
Dugaz, Lucien

Bias, Risks, and Limitations

The data are skewed toward Old French, Middle Dutch and Spanish, specifically from the 14th century.

The only language that is represented over all centuries is Latin, and in each scripts. The other language with a coverage close to Latin is Old French.

Only one document is available in Old English.

Citation [optional]

BibTeX:

@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 Ariane, Pinche and Malamatenia, Vlachou-Efstathiou and Alix, Chagu{\'e} and Jean-Baptiste, Camps and Matthias, Gille-Levenson and Olivier, Brisville-Fertin and Franz, Fischer and Michaels, Gervers and Agn{\`e}s, Boutreux and Avery, Manton and Simon, Gabay and Patricia, O'Connor and Wouter, Haverals and Mike, Kestemont and Caroline, Vandyck and Benjamin, Kiessling},
  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, Pinche Ariane, Vlachou-Efstathiou Malamatenia, Chagué Alix, Camps Jean-Baptiste, et al.. CATMuS Medieval: A multilingual large-scale cross-century dataset in Latin script for handwritten text recognition and beyond. 2024. ⟨hal-04453952⟩

Glossary [optional]

Scripts: In the middle ages, the writing style changed over time, specifically in "litterary" manuscripts, for which we call the general scripts "Bookscripts". This is what CATMuS Medieval covers at the time

Dataset Card Contact

Thibault Clérice ([email protected])