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Tags:
computer-vision
object-detection
document-layout-analysis
codicology
digital-humanities
historical-documents
License:
Dataset Viewer
image_path
stringlengths 11
39
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imagewidth (px) 757
11k
| width
int32 757
11k
| height
int32 1.15k
13.7k
| objects
sequence | source_corpus
class label 3
classes |
|---|---|---|---|---|---|
train_000945.jpg
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train_000758.jpg
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FRAN_0393_04855_L.jpg
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train_000214.jpg
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btv1b10038211z_f129.jpg
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train_000127.jpg
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End of preview. Expand
in Data Studio
A Unified Dataset for Codicological Document Layout Analysis
Dataset Description
This repository contains a large-scale, unified dataset for Document Layout Analysis (DLA) in historical manuscripts. It was created by harmonizing three distinct public corpora—e-NDP, CATMuS, and HORAE—which cover a wide range of document types from the 12th to the 17th century (administrative registers, literary manuscripts, printed books, and Books of Hours).
The key feature of this dataset is its hierarchical, multi-label annotation scheme. Unlike traditional flat object detection datasets, each object is annotated with its full path in a codicologically-informed ontology, enabling the training of sophisticated, semantically-aware models. For example, a simple initial is tagged with three labels: Initial, Initial_Manuscript, and Initial_Ms_Simple.
This dataset was created to train and evaluate the YOLO-gen foundational model and is intended to serve as a challenging new benchmark for the DLA community.
Training Data
The corpus harmonization was achieved through a custom hierarchical ontology described in the accompanying paper. The original annotations were transformed to Oriented Bounding Box (OBB) annotations in order to better detect complex manuscripts forms.
- e-NDP: A corpus of Parisian medieval registers (1326-1504) with a relatively homogeneous administrative layout.
- CATMuS: A diverse multi-class dataset derived from various medieval and modern sources (ca. 12th-17th c.), including administrative, literary, and printed documents.
- HORAE: A corpus of richly decorated Books of Hours (ca. 13th-16th c.) with complex and artistic layouts.
Dataset Structure
The dataset is organized into three splits: train, validation (a 5% sample of the original training data), and test. Each split contains images and labels subfolders. The annotations follow the YOLO OBB (Oriented Bounding Box) format.
Label Format
Each .txt label file can contain multiple lines for a single object, corresponding to its hierarchical labels. The format for each OBB annotation line is:
<class_id> <x1> <y1> <x2> <y2> <x3> <y3> <x4> <y4>
Where (x_i, y_i) are the normalized coordinates of the four corners of the oriented bounding box.
Class Ontology
The V7 class structure was designed to harmonize the three disparate source datasets by reclassifying their original, non-interoperable labels into a single, coherent, and hierarchical ontology. The methodology is informed by established codicological vocabulary, allowing the model to learn relationships between layout elements.
The hierarchy is structured as follows:
- Parent Classes: These are the highest-level categories, representing fundamental codicological concepts like
Text,Decoration, andInitial. They provide a shared, abstract vocabulary that is common across all document types. - Intermediate Parents: In some cases, a second hierarchical level like
ParatextorInitial_Manuscriptwas introduced. This serves to group elements by their function (e.g., separating main text from paratextual elements) or by their visual style (e.g., separating manuscript initials from printed ones), providing a necessary layer of nuance. - Subclasses: These are the "leaf nodes" of the hierarchy (e.g.,
Text_Main,Paratext_Marginal,Initial_Ms_Simple). They are the direct mapping of the granular labels from the source corpora, preserving their original specificity within the new, structured framework.
This results in a multi-label dataset. Each object annotation includes its full hierarchical path. For instance, a marginal note from the e-NDP corpus is tagged with three labels: Text, Paratext, and Paratext_Marginal. This rich annotation scheme enables the training of powerful generalist models like YOLO-gen, which can understand the context and relationships between layout elements, and serves as a blueprint for future data harmonization efforts in the Digital Humanities.
| ID | Class Name | Hierarchy | Source Corpora |
|---|---|---|---|
| 0 | Text |
PARENT | - |
| 1 | Decoration |
PARENT | - |
| 2 | Initial |
PARENT | - |
| 3 | Marks |
PARENT | - |
| 4 | Damage |
PARENT | - |
| 5 | Numbering |
PARENT | - |
| 6 | Paratext |
Text > INTERMEDIATE |
- |
| 7 | Initial_Manuscript |
Initial > INTERMEDIATE |
- |
| 8 | Initial_Printed |
Initial > INTERMEDIATE |
- |
| 9 | Text_Main |
Text |
E, C |
| 10 | Deco_Border |
Decoration |
H |
| 11 | Deco_Miniature |
Decoration |
H |
| 12 | Deco_Graphic |
Decoration |
C |
| 13 | Deco_LineFiller |
Decoration |
H |
| 14 | Initial_Ms_Simple |
Initial > Initial_Manuscript |
H |
| 15 | Initial_Ms_Decorated |
Initial > Initial_Manuscript |
H |
| 16 | Initial_Ms_Historiated |
Initial > Initial_Manuscript |
H |
| 17 | Initial_P_DropCapital |
Initial > Initial_Printed |
C |
| 18 | Marks_Quire |
Marks |
C |
| 19 | Marks_Stamp |
Marks |
C |
| 20 | Marks_Seal |
Marks |
C |
| 21 | Numbering_Page |
Numbering |
E, C |
| 22 | Damage_Generic |
Damage |
C |
| 23 | Damage_ScanArtifact |
Damage |
C |
| 24 | Paratext_Marginal |
Text > Paratext |
E, C, H |
| 25 | Paratext_Header |
Text > Paratext |
C |
| 26 | Paratext_List |
Text > Paratext |
E |
| 27 | Paratext_DateLine |
Text > Paratext |
E |
- Source Corpora Key: E (e-NDP), C (CATMuS), H (HORAE).
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