File size: 2,930 Bytes
1dd5fef f6a267f a0036c9 aff40a1 a0036c9 aff40a1 a0036c9 f87fbcc aff40a1 f87fbcc aff40a1 a0036c9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 |
---
license: apache-2.0
task_categories:
- text-classification
- translation
language:
- en
size_categories:
- 1M<n<10M
---
## Data Introduction
Over 1.5 Million synthetically generated ground-truth/[OCR](https://en.wikipedia.org/wiki/Optical_character_recognition) pairs for post correction tasks from our paper "[Large Synthetic Data from the ar𝜒iv for OCR Post Correction of Historic Scientific Articles](https://dl.acm.org/doi/10.1007/978-3-031-43849-3_23)".
Synthetic ground truth (SGT) sentences have been mined from the [ar𝜒iv Bulk Downloads](https://info.arxiv.org/help/bulk_data/index.html) source documents,
and Optical Character Recognition (OCR)
sentences have been generated with the [Tesseract](https://github.com/tesseract-ocr/tesseract) OCR engine on the PDF pages generated from compiled source documents.
SGT/OCR pairs come from astronomy articles in the years 1991-2011.
No page augmentation has been applied to any of the PDF documents (i.e. these are "clean" pages without warping, dust, etc.)
## Resources
### Dataset Versions
* V0 (original released with original paper) is available [here](https://zenodo.org/records/8006584)
## Citation
Please reference the following if you make use of this dataset:
```
@inproceedings{10.1007/978-3-031-43849-3_23,
author = {Naiman, J. P. and Cosillo, Morgan G. and Williams, Peter K. G. and Goodman, Alyssa},
title = {Large Synthetic Data From the arχiv For OCR Post Correction Of Historic Scientific Articles},
year = {2023},
isbn = {978-3-031-43848-6},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-031-43849-3_23},
doi = {10.1007/978-3-031-43849-3_23},
abstract = {Historical scientific articles often require Optical Character Recognition (OCR) to transform scanned documents into machine-readable text, a process that often produces errors. We present a pipeline for the generation of a synthetic ground truth/OCR dataset to correct the OCR results of the astrophysics literature holdings of the NASA Astrophysics Data System (ADS). By mining the arχiv we create, to the authors’ knowledge, the largest scientific synthetic ground truth/OCR post correction dataset of 203,354,393 character pairs. Baseline models trained with this dataset find the mean improvement in character and word error rates of 7.71\% and 18.82\% for historical OCR text, respectively. Interactive dashboards to explore the dataset are available online: , and data and code, are hosted on GitHub: .},
booktitle = {Linking Theory and Practice of Digital Libraries: 27th International Conference on Theory and Practice of Digital Libraries, TPDL 2023, Zadar, Croatia, September 26–29, 2023, Proceedings},
pages = {265–274},
numpages = {10},
keywords = {scholarly document processing, optical character recognition, astronomy},
location = {Zadar, Croatia}
}
```
|