This repository is organized into two main data folders: --- 📁 `btv1b84472995_GT.zip` This folder contains the ground truth dataset used for Handwritten Text Recognition (HTR), created from selected folia of the manuscript Paris, BnF, français. 2813 The identifier `btv1b84472995` refers to the ark ID of this manuscript in Gallica. Folder structure: btv1b84472995_GT ├── images └── annotations - `images/`: High-resolution selected images downloaded from Gallica. Image names follow the pattern `btv1b84472995_f`, corresponding to the Gallica view number. ➤ Credit: *Source gallica.bnf.fr / Bibliothèque nationale de France* - `annotations/`: XML-ALTO annotation files created with eScriptorium. Layout: Annotations follow the [**Segmonto**](https://segmonto.github.io/) ontology. The potential users of the ground truth should note that we use additional personalized tags for: - `'RubricLines'`: Rubricated lines - `'HalfLines'`: Partial or incomplete lines Transcription: The dataset is CATMuS-compliant, using a graphemic transcription approach. --- 📁 `dataset.zip` This folder contains the dataset used in the experiments described in the paper, using the Learnable Typewriter architecture. Folder structure: dataset ├── images └── annotation.json - `images/`: Each subfolder contains polygonal line extractions (with alpha transparency) per manuscript page.- `annotation.json`: Contains the annotation and metadata for each line. `annotation.json` structure example: ```json"": { // corresponds to the image names in the images folders "split": "train", "label": "A beautiful calico cat.",// Transcription text of the line "line_type": "DefaultLine",// The type of line "script": "RaouletOrleans", // Identifier for the scribal hand "folio": "1r", "gp": "GP1", "doc": "HT1" } ** add project webpage ** Code: https://github.com/malamatenia/palaeographic-variability-analysis-grandes-chroniques-fr-2813This study was supported by the CNRS through MITI and the 80|Prime program (CrEMe Caractérisation des écritures médiévales), and by the European Research Council (ERC project DISCOVER, number 101076028).