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In this paper we describe a dataset of German and Latin \textit{ground truth}(GT) for historical OCR in the form of printed text line images paired withtheir transcription. This dataset, called \textit{GT4HistOCR}, consists of313,173 line pairs covering a wide period of printing dates from incunabulafrom the 15th century to 19th century books printed in Fraktur types and isopenly available under a CC-BY 4.0 license. The special form of GT as lineimage/transcription pairs makes it directly usable to train state-of-the-artrecognition models for OCR software employing recurring neural networks in LSTMarchitecture such as Tesseract 4 or OCRopus. We also provide some pretrainedOCRopus models for subcorpora of our dataset yielding between 95\% (earlyprintings) and 98\% (19th century Fraktur printings) character accuracy rateson unseen test cases, a Perl script to harmonize GT produced by differenttranscription rules, and give hints on how to construct GT for OCR purposeswhich has requirements that may differ from linguistically motivatedtranscriptions.

Ground Truth for training OCR engines on historical documents in German Fraktur and Early Modern Latin