DOI: 10.5281/zenodo.17279363
title: HORAE_Minit. Book of hours, Miniatures and Initials: dataset

description: HORAE_Minit (Books of Hours, Miniatures and Initials) is a comprehensive annotated dataset for automatic detection and classification of decorative elements in medieval Books of Hours manuscripts. Created at the Institut de Recherche et d'Histoire des Textes (IRHT-CNRS) as part of the Biblissima+ ÉquipEx, this dataset enables large-scale computational analysis of medieval illuminations.

Dataset Overview



14,225 images from over 1,000 manuscripts

105,682 annotations across 6 classes of decorative elements

12,270 annotated images with detailed bounding boxes

YOLO-compatible format for immediate use with state-of-the-art detection models

Complete IIIF traceability with URLs to original digital libraries


Detection Classes

The dataset includes six classes of decorative elements commonly found in Books of Hours:



Miniature (1,186 instances): Narrative scenes located in the text field

Historiated initial (496 instances): Initials containing animated scenes

Ornamented initial (78,274 instances): Decorated initials with penwork, champie, or floral motifs

Simple initial (23,121 instances): Colored initials without additional decoration

Marginal medallion (878 instances): Framed marginal scenes

Marginal decoration (1,624 instances): Unframed animated marginal elements (zoomorphic, heraldic)


Annotation Methodology

Annotations were created through an iterative human-in-the-loop process:



Manual annotation of subset A by domain experts

Training of detection models

Automatic pre-annotation of new manuscripts

Systematic manual validation with multiple quality control measures


The annotation ontology was specifically designed to balance traditional codicological terminology with computational constraints and large-scale annotation requirements. All decisions are documented in the accompanying article.

Data Sources

Images were downloaded from IIIF-compliant digital libraries, primarily:



Institut de Recherche et d'Histoire des Textes (IRHT-CNRS): ~12,230 images

Bibliothèque nationale de France (Gallica): ~1,090 images

The Walters Art Museum (via Stanford): ~236 images

30+ additional institutions across Europe and North America


All images include metadata with original IIIF URLs, dimensions, manifest references, and canvas identifiers, ensuring full traceability and reproducibility.

File Formats



Images: JPEG format, minimum dimension 1200 pixels

Annotations: YOLO format (TXT files with normalized coordinates)

Metadata: CSV files with comprehensive image and annotation information

Visualizations: HTML files for browsing annotations by class or manuscript


Technical Specifications



Annotation type: Rectangular bounding boxes (YOLO format)

Coordinate system: Normalized (0-1) relative to image dimensions

Resolution: Downloaded at maximum IIIF resolution, resized to min. 1200px

File naming: Systematic pattern enabling manuscript identification

Quality control: Multiple validation passes, overlap detection, visual inspection


Use Cases

This dataset enables:

Digital Humanities Research:



Quantitative codicology: systematic analysis of decoration density and distribution

Comparative studies across manuscripts, regions, and time periods

Statistical analysis of decorative programs


Heritage Applications:



Automated indexing of manuscript collections

Enhanced search and discovery interfaces

Digital exhibition curation


Computer Vision Research:



Object detection in historical documents

Handling of class imbalance in cultural heritage data

Transfer learning from medieval manuscripts


Art History:



Large-scale iconographic analysis

Study of workshop practices and artistic attribution

Analysis of text-image relationships


Performance Benchmarks

Models trained on this dataset achieve:



Weighted F1 > 0.90 on majority classes (reflecting real-world annotation workflows)

Standard F1: 0.64-0.80 across all classes (accounting for minority class challenges)

Precision > 0.88 and Recall > 0.92 on ornamented initials


See related publication (HORAE Detection Models) for detailed performance analysis.







Related Publications



HORAE_LSv2 Dataset (predecessor): https://doi.org/10.5281/zenodo.16919911

HORAE Detection Models (trained on this dataset): https://doi.org/10.5281/zenodo.17279775

Boillet et al. (2019): "HORAE: an annotated dataset of books of hours", HIP'19


 

@article{bernard2025horae_detection,  title        = {{Detection of Miniatures and Initials in Medieval                    Books of Hours: Ontology, Datasets, and Models HORAE_Minit}},  author       = {Bernard-Leterme, Lise and Stutzmann, Dominique},  journal      = {Humanités numériques},  year         = 2025,  note         = {submitted}}