DenisaBumba/rfdetr-segmentation-leibniz-dataset

Dataset Card for Leibniz's Manuscripts (Instance Segmentation Dataset)

This dataset comprises instance segmentation annotations in raw COCO format, used to train an RF-DETR-Seg-nano model for the automatic recognition of textual, graphical, and mathematical expression zones within the manuscripts of the philosopher and mathematician Gottfried Wilhelm Leibniz (17th-early 18th c.).

Dataset Details

Dataset Description

• —Curated by: Bumba, Denisa-Florina
• —Funded by [optional]: (European Research Council, 101020985)
• —Shared by [optional]: ERC PHILIUMM Project
• —Language(s) (NLP): Latin, French (17th-early 18th c.)
• —License: cc-by-4.0

Uses

Direct Use

• —This dataset is intended for the detection and delimitation of textual, graphical, and mathematical zones within Leibniz's manuscripts, and more broadly within early-modern manuscript sources.

Out-of-Scope Use

• —Has never been tested on modern printed or handwritten textes.

Dataset Structure

train/_annotations.coco.json + images
valid/_annotations.coco.json + images
test/_annotations.coco.json  + images

Classes

Class names follow the SegmOnto controlled vocabulary for manuscript layout annotation.

Preprocessing

• —Auto-orient applied
• —Resized (stretch) to 728×728

Augmentation

(applied to the training set only, 2 augmented outputs generated per source image):

• —Horizontal and vertical flip
• —Rotation: ±3°
• —Shear: ±5° horizontal, ±5° vertical
• —Hue: ±15°
• —Brightness: ±15%
• —Noise: up to 1.49% of pixels

Class Distribution before data augmentation

Class Distribution after data augmentation

Annotation counts per category and split, computed after data augmentation.

Data Collection and Processing

Who are the source data producers?

Image annotation 343 folios with mainly text zones and model training by Denisa-Florina BUMBA within the ERC Philiumm Project (Laboratoire SPHERE, Univ. Paris Cité - CNRS, France). Project manager: David RABOUIN

Image annotation of 59 pages containing mainly mathematical zones by Yunfan LI, under the guidance of Yejing XIE, Harold MOUCHÈRE (Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000, Nantes, France)

Annotation process

Bias, Risks, and Limitations

GraphicZone-formula is a generic label for formulae that weren't sorted into one of the three more specific classes (-complex, -inline, -strikethrough). Since annotations came from different practices and different annotators, not all formulae were manually reclassified. This is a known issue we plan to fix in a future version of the dataset.

Funding

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme, Advanced Grant ADG No. 101020985.