create datasheet

README.md

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----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+task_categories:
+- image-segmentation
+pretty_name: page_extraction
+size_categories:
+- 1K<n<10K
+tags:
+- computer_vision
+- document_layout_analysis
+---
+# Title
+Page Extraction Dataset
+
+# Description
+In digitised cultural heritage items such as books, newspapers and archival records, a problem that can negatively affect OCR are black margins around a page caused by document scanning. In order to enable document layout analysis (DLA), these black margins need to be cropped and the pages need to be extracted correctly. To enable the training of a machine learning model capable of extracting pages, a dataset was created. The machine learning task for which this dataset was collected falls into the domain of image segmentation and, more generally, of computer vision. The dataset was compiled by Vahid Rezanezhad within the research project "[Mensch.Maschine.Kultur – Künstliche Intelligenz für das Digitale Kulturelle Erbe](https://mmk.sbb.berlin/?lang=en)" at the Staatsbibliothek zu Berlin – Berlin State Library (SBB). The research project was funded by the Federal Government Commissioner for Culture and the Media (BKM), project grant no. 2522DIG002. The Minister of State for Culture and the Media is part of the German Federal Government. 
+
+## Homepage
+[Eynollah – Document Layout Analysis with Deep Learning and Heuristics](https://github.com/qurator-spk/eynollah)
+
+## Publisher
+Staatsbibliothek zu Berlin – Berlin State Library
+
+## Dataset Curators
+Dr. Vahid Rezanezhad, postdoctoral researcher, Staatsbibliothek zu Berlin – Berlin State Library, [Vahid.Rezanezhad@sbb.spk-berlin.de](mailto:Vahid.Rezanezhad@sbb.spk-berlin.de), ORCID: [0009−0007−8041−6506](https://orcid.org/0009−0007−8041−6506). Vahid Rezhanezhad has studied computational physics at the University of Potsdam, where he received a Ph.D. in 2016. Between 2019 and 2022, he has worked in the QURATOR project and is now employed in the research project "Mensch.Maschine.Kultur" at Berlin State Library; he was responsible for preparing the dataset, labeling and model training.
+
+Dr. Jörg Lehmann, postdoctoral researcher, Staatsbibliothek zu Berlin – Berlin State Library, [Joerg.Lehmann@sbb.spk-berlin.de](mailto:Joerg.Lehmann@sbb.spk-berlin.de), ORCID: [0000-0003-1334-9693](https://orcid.org/0000-0003-1334-9693). Jörg Lehmann has studied history and comparative literature and works in the research project "Mensch.Maschine.Kultur"; he was responsible for preparing the data for publication and drafting the datasheet. 
+
+## Other Contributors
+Contributions: [The Europeana Newspapers Project Dataset](https://www.primaresearch.org/datasets/ENP); [Poznán Supercomputing and Networking Center (PSNC) Digital Libraries in the frame of the IMPACT project](https://web.archive.org/web/20220520105522/https://dl.psnc.pl/activities/projekty/impact/results/); [Dataset for dhSegment (Ares Oliveira & Seguin)](https://github.com/dhlab-epfl/dhSegment/releases/); Vahid Rezanezhad, project collaborator in the "Human.Machine.Culture" project at Staatsbibliothek zu Berlin – Berlin State Library. 
+
+## Point of Contact
+Clemens Neudecker, Staatsbibliothek zu Berlin – Berlin State Library, [Clemens.Neudecker@sbb.spk-berlin.de](mailto:Clemens.Neudecker@sbb.spk-berlin.de), ORCID: [0000-0001-5293-8322](https://orcid.org/0000-0001-5293-8322) 
+
+## Papers and/or Other References
+Clausner, C., Papadopoulos, C., Pletschacher, S., & Antonacopoulos, A. (2015). The ENP image and ground truth dataset of historical newspapers. 2015 13th International Conference on Document Analysis and Recognition (ICDAR), 931–935. [https://doi.org/10.1109/ICDAR.2015.7333898](https://doi.org/10.1109/ICDAR.2015.7333898) 
+
+Oliveira, S. A., Seguin, B., & Kaplan, F. (2018). dhSegment: A Generic Deep-Learning Approach for Document Segmentation. 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), Niagara Falls, NY, USA, 2018, 7–12. [https://doi.org/10.1109/ICFHR-2018.2018.00011](https://doi.org/10.1109/ICFHR-2018.2018.00011) 
+
+Papadopoulos, C., Pletschacher, S., Clausner, C., & Antonacopoulos, A. (2013). The IMPACT dataset of historical document images. Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing, 123–130. [https://doi.org/10.1145/2501115.2501130](https://doi.org/10.1145/2501115.2501130) 
+
+Rezanezhad, V., Baierer, K., Gerber, M., Labusch, K., & Neudecker, C. (2023). Document Layout Analysis with Deep Learning and Heuristics. Proceedings of the 7th International Workshop on Historical Document Imaging and Processing (HIP ‘23) Association for Computing Machinery, New York, 73–78. [https://doi.org/10.1145/3604951.3605513](https://doi.org/10.1145/3604951.3605513)
+
+## Supported Tasks and Shared Tasks
+This dataset was not part of a shared task.
+
+### AI Category 
+[image segmentation](https://huggingface.co/tasks/image-segmentation)
+
+### Type of Cultural Heritage Application
+[computer vision](https://ai4culture.eu/resources?aiCategories=COMPUTER_VISION)
+
+### (Cultural Heritage) Application Example
+page extraction
+
+# Distribution
+This dataset is distributed by the two named data curators. It is planned to cite the dataset in upcoming publications. The dataset fully complies with the European General Data Protection Regulation GDPR.
+
+## Data Access URL
+[https://doi.org/10.5281/zenodo.15094542](https://doi.org/10.5281/zenodo.15094542)
+
+## Licensing Information
+[Creative Commons Attribution 4.0 International CC BY 4.0.](https://creativecommons.org/licenses/by/4.0/legalcode) Except for the data coming from Berlin State Library (SBB), all the original image files used for creating this dataset have previously been published separately under individual terms and conditions and have therefore not been included in this data publication. 
+
+## File Format
+image/jpg, image/png
+
+## Citation Information
+```
+@dataset{rezanezhad_2026_15094542,
+  author       = {Rezanezhad, Vahid and
+                  Lehmann, Jörg},
+  title        = {Page Extraction Dataset},
+  month        = jan,
+  year         = 2026,
+  publisher    = {Staatsbibliothek zu Berlin - Berlin State Library},
+  version      = 1,
+  doi          = {10.5281/zenodo.15094542},
+  url          = {https://doi.org/10.5281/zenodo.15094542},
+}
+```
+
+# Composition
+
+## Data Category 
+content
+
+## Media Category
+image
+
+## Object Type
+books, newspapers, historical documents
+
+## Dataset Structure
+The complete training dataset consists of 3.820 original image files in both .tif and .jpg format, in 1.000 binarized images in .png format and in 3.820 labeled images in .png format. 2.029 original image files were taken from the dataset for dhSegment, 505 original image files were taken from the Europeana Newspapers Project (ENP), 286 from the Poznán Supercomputing and Networking Center (PSNC) dataset compiled within the frame of the IMPACT Project, and 1.000 images from the Berlin State Library (SBB). However, as all the original image files used for creating this dataset have previously been published separately except for the SBB images, this data publication consists only of the 1.000 SBB images as well as the labeled image files and does not comprise the original image files provided by the above-named sources, where the original image files can be requested or downloaded via the links provided above. Moreover, this data publication includes a .csv file containing all the names of the original images as well as their provenance that were used as training data in order to enable a replication of the model training. 
+
+### Data Instances
+Not applicable.
+
+### Data Fields
+Not applicable.
+
+### Compliance with Standard(s)
+Not applicable.
+
+### Data Splits
+Not applicable.
+
+## Languages
+deu, eng, pol, fra, lat, ita ([ISO 639-2](https://www.loc.gov/standards/iso639-2/php/code_list.php))
+
+## Descriptive Statistics
+Data can be found in three directories: The first directory is named "images" and contains the 1.000 previously unpublished image files from SBB in .jpg format; the second directory is named "images_bin" and contains the same 1.000 images like the first directory, only in binarized form and in .png format; the third directory is named "labels" and contains 3.820 annotated files in .png format. The compressed file amounts up to 1,89 GB. Metadata are not part of the data publication.
+
+# Data Collection Process
+
+## Curation Rationale
+The motivation for creating this dataset was to compile examples of digitised historical documents with black margins around a page with the aim to train a machine learning model capable of extracting the pages from these document scans.
+
+## Source Data
+
+
+### Initial Data Collection
+The source data were collected by three independent projects focusing on optimising the performance of optical character recognition (OCR) and image segmentation on historical documents: The [Europeana Newspapers Project Dataset](https://www.primaresearch.org/datasets/ENP); the [Poznán Supercomputing and Networking Center (PSNC) Digital Libraries dataset](https://web.archive.org/web/20220520105522/https://dl.psnc.pl/activities/projekty/impact/results/), and the [dataset for dhSegment (Ares Oliveira & Seguin)](https://github.com/dhlab-epfl/dhSegment/releases/). To these 2.820 images, 1.000 images from a German-language newspaper were added from the collections held by SBB. The collected documents were printed in Antiqua and Gothic or were handwritten and are in several languages; in part, they were provided alongside with ground truth for OCR. However, this ground truth was not used for the dataset provided here. Rather, from the datasets published by these projects, individual files were selected and annotated in order to provide a broad range of examples suitable to train a model performing the task of page extraction. No modification or normalisation of the selected files was performed. The complete training dataset can be reassembled by collecting the data from the named sources and selecting the individual files using the file listing.
+
+### Source Data Producers
+The source data were produced by humans within the named research projects. The initial intent of source data collection was to provide ground truth for OCR and image segmentation. However, in this case, the ground truth data were not used.
+
+### Digitisation Pipeline
+Not applicable.
+
+## Preprocessing and Cleaning
+No preprocessing of the original image files was performed. 
+
+## Annotations
+As described above in the section "Initial Data Collection" and "Who are the source data producers?", the published dataset consists mainly of annotations. In the case of the ENP and IMPACT-PSNC files, those annotations were not part of the original datasets.
+
+### Annotation Process
+All 3.820 files have been annotated by Vahid Rezanezhad. A part of the labels were reused from the dhSegment dataset where the labels are available in .png-files. For the rest of the images, Vahid has annotated bounding boxes of page elements using the tool [labelme](https://github.com/wkentaro/labelme), which were then written in a .json-file; then they were again encoded in a .png-file. The coordinates of the pages are extracted from the page-xml files and encoded in the .png-files. Each class element in the label like background or page is encoded with an integer.
+
+### Annotators
+The only annotator was the project collaborator Vahid Rezhanezhad; see the information provided about him above.
+
+### Crowd Labour
+Not applicable.
+
+## Data Provenance
+The data of the ENP and the IMPACT-PSNC projects as well as the dhSegment dataset can be obtained via the websites linked above. ENP data were published under their own terms and conditions, IMPACT-PSNC data under a CC-BY-3.0 licence, dhSegment data without any dedicated licence. Republication of these datasets was abdicated; rather, they may be requested by users who are interested in establishing a dataset for comparable tasks or who want to replicate the model training. The 1.000 images from SBB have not yet been published, but are now made available as part of this data publication under a CC-BY 4.0 licence. 
+
+## Use of Linked Open Data, Controlled Vocabulary, Multilingual Ontologies/Taxonomies
+Not applicable.
+
+## Version Information
+There is no previous version of this dataset.
+
+### Release Date
+2026-01-22
+
+### Date of Modification
+Not applicable.
+
+### Checksums
+**MD5 checksum of the page_extraction.tar.gz:** c15cd826cd43d6401f50e7cf2a892b7a
+
+**SHA-256 checksum of the page_extraction.tar.gz:** fe0e0a68ccbda11d80d6b8c3ba552b8155379d8291cd5996ccdf608f6a2514e1
+
+## Maintenance Plan
+
+### Maintenance Level
+Limited Maintenance – The data will not be updated, but any technical issues will be addressed
+
+### Update Periodicity 
+It is not foreseen to update the dataset.
+
+
+# Examples and Considerations for Using the Data
+This dataset consists of image files and .png files with annotations marking the bounding boxes of page elements. The original images contain text which may be relevant for ethical considerations. However, this text was not used for the training of the model, nor was OCR performed to extract the text. The following sections of the datasheet are therefore not applicable.
+
+## Ethical Considerations
+
+### Personal and Other Sensitive Information
+Not applicable.
+
+### Discussion of Biases
+Not applicable.]
+
+### Potential Societal Impact of Using the Dataset
+Not applicable.        	
+
+## Examples of Datasets, Publications and Models that (re-)use the Dataset 
+So far, this dataset has not yet been reused.      	
+
+## Known Non-Ethical Limitations
+Not applicable.
+
+## Unanticipated Uses made of this Dataset
+Not applicable.