ReadingTimeMachine/rtm-sgt-ocr-v1

Tasks

• text-classification
• translation

Citation text from the card

@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, Proceed

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