Ground Truth for training OCR engines on historical documents in German Fraktur and Early Modern Latin

In this paper we describe a dataset of German and Latin ground truth (GT) for historical OCR in the form of printed text line images paired with their transcription. This dataset, called GT4HistOCR, consists of 313,173 line pairs covering a wide period of printing dates om incunabula om the 15th century to 19th century books printed in Fraktur types and is openly available under a CC-BY 0 license. The special form of GT as line image/transcription pairs makes it directly usable to train stateof-the-art recognition models for OCR soware employing recurring neural networks in LSTM architecture such as Tesseract 4 or OCRopus. We also provide some pretrained OCRopus models for subcorpora of our dataset yielding between 95% (early printings) and 98% (19th century Fraktur printings) character accuracy rates on unseen test cases, a Perl script to harmonize GT produced by different transcription rules, and give hints on how to construct GT for OCR purposes which has requirements that may differ om linguistically motivated transcriptions.