Datasets:
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Update README.md
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README.md
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@@ -74,6 +74,7 @@ This dataset contains a subset of data used in the paper [You Actually Look Twic
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- TableZone
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- TitlePageZone
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### Supported Tasks and Leaderboards
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- `object-detection`: This dataset can be used to train a model for object-detection on historic document images.
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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.
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- The first configuration `YOLO
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- The second configuration converts the YOLO format into a format closer to the `COCO` annotation format. This is done
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### Data Instances
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An example instance from the COCO config:
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```
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{'height': 5610,
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'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3782x5610 at 0x7F3B785609D0>,
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'image_id': 0,
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An example instance from the YOLO config:
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```
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{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3782x5610 at 0x7F3B785EFA90>,
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'objects': {'bbox': [[2144, 292, 1198, 170],
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[1120, 1462, 414, 331],
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The fields for the YOLO config:
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- `image`: the image
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- `objects`: the annotations which
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- `bbox`: a list of bounding boxes for the image
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- `label`: a list of labels for this image
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The dataset contains a train, validation and test split with the following numbers per split:
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| | train | validation | test |
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|----------|-------|------------|------|
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| examples | 196 | 22 | 135 |
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## Dataset Creation
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### Curation Rationale
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### Source Data
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#### Initial Data Collection and Normalization
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[More information needed]
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### Annotations
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[More information needed]
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#### Annotation process
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[
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#### Who are the annotators?
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- TableZone
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- TitlePageZone
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### Supported Tasks and Leaderboards
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- `object-detection`: This dataset can be used to train a model for object-detection on historic document images.
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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.
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- The first configuration, `YOLO`, uses the data's original format.
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- 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 the `feature_extractor` from the `Transformers` models for object detection, which expect data to be in a COCO style format.
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### Data Instances
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An example instance from the COCO config:
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```python
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{'height': 5610,
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'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3782x5610 at 0x7F3B785609D0>,
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'image_id': 0,
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An example instance from the YOLO config:
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```python
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{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=3782x5610 at 0x7F3B785EFA90>,
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'objects': {'bbox': [[2144, 292, 1198, 170],
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[1120, 1462, 414, 331],
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The fields for the YOLO config:
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- `image`: the image
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- `objects`: the annotations which consist of:
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- `bbox`: a list of bounding boxes for the image
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- `label`: a list of labels for this image
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The dataset contains a train, validation and test split with the following numbers per split:
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| Dataset | Number of images |
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|---------|------------------|
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| Train | 854 |
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| Dev | 154 |
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| Test | 139 |
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A more detailed summary of the dataset (copied from the paper):
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| | Train | Dev | Test | Total | Average area | Median area |
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|--------------------------|------:|----:|-----:|------:|-------------:|------------:|
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| DropCapitalZone | 1537 | 180 | 222 | 1939 | 0.45 | 0.26 |
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| MainZone | 1408 | 253 | 258 | 1919 | 28.86 | 26.43 |
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| NumberingZone | 421 | 57 | 76 | 554 | 0.18 | 0.14 |
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| MarginTextZone | 396 | 59 | 49 | 504 | 1.19 | 0.52 |
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| GraphicZone | 289 | 54 | 50 | 393 | 8.56 | 4.31 |
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| MusicZone | 237 | 71 | 0 | 308 | 1.22 | 1.09 |
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| RunningTitleZone | 137 | 25 | 18 | 180 | 0.95 | 0.84 |
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| QuireMarksZone | 65 | 18 | 9 | 92 | 0.25 | 0.21 |
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| StampZone | 85 | 5 | 1 | 91 | 1.69 | 1.14 |
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| DigitizationArtefactZone | 1 | 0 | 32 | 33 | 2.89 | 2.79 |
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| DamageZone | 6 | 1 | 14 | 21 | 1.50 | 0.02 |
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| TitlePageZone | 4 | 0 | 1 | 5 | 48.27 | 63.39 |
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## Dataset Creation
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This dataset is derived from:
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- CREMMA Medieval ( Pinche, A. (2022). Cremma Medieval (Version Bicerin 1.1.0) [Data set](https://github.com/HTR-United/cremma-medieval)
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- CREMMA Medieval Lat (Clérice, T. and Vlachou-Efstathiou, M. (2022). Cremma Medieval Latin [Data set](https://github.com/HTR-United/cremma-medieval-lat)
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- Eutyches. (Vlachou-Efstathiou, M. Voss.Lat.O.41 - Eutyches "de uerbo" glossed [Data set](https://github.com/malamatenia/Eutyches)
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- Gallicorpora HTR-Incunable-15e-Siecle ( Pinche, A., Gabay, S., Leroy, N., & Christensen, K. Données HTR incunable du 15e siècle [Computer software](https://github.com/Gallicorpora/HTR-incunable-15e-siecle)
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- Gallicorpora HTR-MSS-15e-Siecle ( Pinche, A., Gabay, S., Leroy, N., & Christensen, K. Données HTR manuscrits du 15e siècle [Computer software](https://github.com/Gallicorpora/HTR-MSS-15e-Siecle)
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- Gallicorpora HTR-imprime-gothique-16e-siecle ( Pinche, A., Gabay, S., Vlachou-Efstathiou, M., & Christensen, K. HTR-imprime-gothique-16e-siecle [Computer software](https://github.com/Gallicorpora/HTR-imprime-gothique-16e-siecle)
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+ a few hundred newly annotated data, specifically the test set which is completely novel and based on early prints and manuscripts.
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These additional annotations were created by correcting an early version of the model developed in the paper using the [roboflow](https://roboflow.com/) platform.
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### Curation Rationale
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### Source Data
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The sources of the data are described above.
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#### Initial Data Collection and Normalization
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[More information needed]
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### Annotations
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#### Annotation process
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Additional annotations produced for this dataset were created by correcting an early version of the model developed in the paper using the [roboflow](https://roboflow.com/) platform.
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#### Who are the annotators?
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