DenisaB/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

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.

Class	Description
MainZone	Main body text region
MarginTextZone	Marginal additions
NumberingZone	Page numbering
DigitizationArtefactZone	Digitization artifacts
GraphicZone-figure	Diagrams, other graphic zones
GraphicZone-formula	Covers all mathematical formulae
GraphicZone-formula-complex	Complex/multi-line formulae
GraphicZone-formula-inline	Inline formulae within text
GraphicZone-formula-strikethrough	Struck-through/deleted formulae

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

Category	Train	Valid	Test	Total
GraphicZone-formula-inline	1196	100	134	1430
GraphicZone-formula-strikethrough	694	56	100	850
MarginTextZone	710	77	43	830
MainZone	477	53	26	556
GraphicZone-figure	382	51	25	458
NumberingZone	313	31	16	360
DigitizationArtefactZone	200	22	10	232
GraphicZone-formula-complex	107	12	4	123
GraphicZone-formula	84	7	0	91
Total	4163	409	358	4930

Class Distribution after data augmentation

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

Category	Train	Valid	Test	Total
GraphicZone-formula-inline	3587	100	134	3821
MarginTextZone	2132	77	43	2252
GraphicZone-formula-strikethrough	2084	56	100	2240
MainZone	1432	53	26	1511
GraphicZone-figure	1146	51	25	1222
NumberingZone	939	31	16	986
DigitizationArtefactZone	600	22	10	632
GraphicZone-formula-complex	321	12	4	337
GraphicZone-formula	252	7	0	259
Total	12493	409	358	13260

Split	Images (incl. augmented)	Unique Source Images
Train	1,026	342
Valid	40	40
Test	20	20
Total	1,086	402

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.