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Scientific articles published prior to the "age of digitization" (~1997)require Optical Character Recognition (OCR) to transform scanned documents intomachine-readable text, a process that often produces errors. We develop apipeline for the generation of a synthetic ground truth/OCR dataset to correctthe OCR results of the astrophysics literature holdings of the NASAAstrophysics Data System (ADS). By mining the arXiv we create, to the authors'knowledge, the largest scientific synthetic ground truth/OCR post correctiondataset of 203,354,393 character pairs. We provide baseline models trained withthis dataset and find the mean improvement in character and word error rates of7.71% and 18.82% for historical OCR text, respectively. When used to classifyparts of sentences as inline math, we find a classification F1 score of 77.82%.Interactive dashboards to explore the dataset are available online:https://readingtimemachine.github.io/projects/1-ocr-groundtruth-may2023, anddata and code, within the limitations of our agreement with the arXiv, arehosted on GitHub: https://github.com/ReadingTimeMachine/ocr_post_correction.

Large Synthetic Data from the arXiv for OCR Post Correction of Historic Scientific Articles