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] |
YALTAi Segmonto Manuscript and Early Printed Book Dataset
Dataset Summary
This dataset contains a subset of data used in the paper You Actually Look Twice At it (YALTAi): using an object detection approach instead of region segmentation within the Kraken engine. This paper proposes treating page layout recognition on historical documents as an object detection task (compared to the usual pixel segmentation approach). This dataset contains images from digitised manuscripts and early printed books with the following labels:
- DamageZone
- DigitizationArtefactZone
- DropCapitalZone
- GraphicZone
- MainZone
- MarginTextZone
- MusicZone
- NumberingZone
- QuireMarksZone
- RunningTitleZone
- SealZone
- StampZone
- TableZone
- TitlePageZone
Supported Tasks and Leaderboards
object-detection
: This dataset can be used to train a model for object-detection on historic document images.
Dataset Structure
This dataset has two configurations. These configurations both cover the same data and annotations but provide these annotations in different forms to make it easier to integrate the data with existing processing pipelines.
- The first configuration,
YOLO
, uses the data's original format. - The second configuration converts the YOLO format into a format closer to the
COCO
annotation format. This is done to make it easier to work with thefeature_extractor
from theTransformers
models for object detection, which expect data to be in a COCO style format.
Data Instances
An example instance from the COCO config:
{'height': 5610,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3782x5610 at 0x7F3B785609D0>,
'image_id': 0,
'objects': [{'area': 203660,
'bbox': [1545.0, 207.0, 1198.0, 170.0],
'category_id': 9,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 137034,
'bbox': [912.0, 1296.0, 414.0, 331.0],
'category_id': 2,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 110865,
'bbox': [2324.0, 908.0, 389.0, 285.0],
'category_id': 2,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 281634,
'bbox': [2308.0, 3507.0, 438.0, 643.0],
'category_id': 2,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 5064268,
'bbox': [949.0, 471.0, 1286.0, 3938.0],
'category_id': 4,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 5095104,
'bbox': [2303.0, 539.0, 1338.0, 3808.0],
'category_id': 4,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []}],
'width': 3782}
An example instance from the YOLO config:
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3782x5610 at 0x7F3B785EFA90>,
'objects': {'bbox': [[2144, 292, 1198, 170],
[1120, 1462, 414, 331],
[2519, 1050, 389, 285],
[2527, 3828, 438, 643],
[1593, 2441, 1286, 3938],
[2972, 2444, 1338, 3808]],
'label': [9, 2, 2, 2, 4, 4]}}
Data Fields
The fields for the YOLO config:
image
: the imageobjects
: the annotations which consist of:bbox
: a list of bounding boxes for the imagelabel
: a list of labels for this image
The fields for the COCO config:
height
: height of the imagewidth
: width of the imageimage
: imageimage_id
: id for the imageobjects
: annotations in COCO format, consisting of a list containing dictionaries with the following keys:bbox
: bounding boxes for the imagescategory_id
: a label for the imageimage_id
: id for the imageiscrowd
: COCO is a crowd flagsegmentation
: COCO segmentation annotations (empty in this case but kept for compatibility with other processing scripts)
Data Splits
The dataset contains a train, validation and test split with the following numbers per split:
Dataset | Number of images |
---|---|
Train | 854 |
Dev | 154 |
Test | 139 |
A more detailed summary of the dataset (copied from the paper):
Train | Dev | Test | Total | Average area | Median area | |
---|---|---|---|---|---|---|
DropCapitalZone | 1537 | 180 | 222 | 1939 | 0.45 | 0.26 |
MainZone | 1408 | 253 | 258 | 1919 | 28.86 | 26.43 |
NumberingZone | 421 | 57 | 76 | 554 | 0.18 | 0.14 |
MarginTextZone | 396 | 59 | 49 | 504 | 1.19 | 0.52 |
GraphicZone | 289 | 54 | 50 | 393 | 8.56 | 4.31 |
MusicZone | 237 | 71 | 0 | 308 | 1.22 | 1.09 |
RunningTitleZone | 137 | 25 | 18 | 180 | 0.95 | 0.84 |
QuireMarksZone | 65 | 18 | 9 | 92 | 0.25 | 0.21 |
StampZone | 85 | 5 | 1 | 91 | 1.69 | 1.14 |
DigitizationArtefactZone | 1 | 0 | 32 | 33 | 2.89 | 2.79 |
DamageZone | 6 | 1 | 14 | 21 | 1.50 | 0.02 |
TitlePageZone | 4 | 0 | 1 | 5 | 48.27 | 63.39 |
Dataset Creation
This dataset is derived from:
- CREMMA Medieval ( Pinche, A. (2022). Cremma Medieval (Version Bicerin 1.1.0) Data set
- CREMMA Medieval Lat (Clérice, T. and Vlachou-Efstathiou, M. (2022). Cremma Medieval Latin Data set
- Eutyches. (Vlachou-Efstathiou, M. Voss.Lat.O.41 - Eutyches "de uerbo" glossed Data set
- Gallicorpora HTR-Incunable-15e-Siecle ( Pinche, A., Gabay, S., Leroy, N., & Christensen, K. Données HTR incunable du 15e siècle Computer software
- Gallicorpora HTR-MSS-15e-Siecle ( Pinche, A., Gabay, S., Leroy, N., & Christensen, K. Données HTR manuscrits du 15e siècle Computer software
- Gallicorpora HTR-imprime-gothique-16e-siecle ( Pinche, A., Gabay, S., Vlachou-Efstathiou, M., & Christensen, K. HTR-imprime-gothique-16e-siecle Computer software
- a few hundred newly annotated data, specifically the test set which is completely novel and based on early prints and manuscripts.
These additional annotations were created by correcting an early version of the model developed in the paper using the roboflow platform.
Curation Rationale
[More information needed]
Source Data
The sources of the data are described above.
Initial Data Collection and Normalization
[More information needed]
Who are the source language producers?
[More information needed]
Annotations
Annotation process
Additional annotations produced for this dataset were created by correcting an early version of the model developed in the paper using the roboflow platform.
Who are the annotators?
[More information needed]
Personal and Sensitive Information
This data does not contain information relating to living individuals.
Considerations for Using the Data
Social Impact of Dataset
A growing number of datasets are related to page layout for historical documents. This dataset offers a different approach to annotating these datasets (focusing on object detection rather than pixel-level annotations). Improving document layout recognition can have a positive impact on downstream tasks, in particular Optical Character Recognition.
Discussion of Biases
Historical documents contain a wide variety of page layouts. This means that the ability of models trained on this dataset to transfer to documents with very different layouts is not guaranteed.
Other Known Limitations
[More information needed]
Additional Information
Dataset Curators
Licensing Information
Creative Commons Attribution 4.0 International
Citation Information
@dataset{clerice_thibault_2022_6814770,
author = {Clérice, Thibault},
title = {{YALTAi: Segmonto Manuscript and Early Printed Book
Dataset}},
month = jul,
year = 2022,
publisher = {Zenodo},
version = {1.0.0},
doi = {10.5281/zenodo.6814770},
url = {https://doi.org/10.5281/zenodo.6814770}
}
Contributions
Thanks to @davanstrien for adding this dataset.
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