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 with
their transcription. This dataset, called \textit{GT4HistOCR}, consists of
313,173 line pairs covering a wide period of printing dates from incunabula
from the 15th century to 19th century books printed in Fraktur types and is
openly available under a CC-BY 4.0 license. The special form of GT as line
image/transcription pairs makes it directly usable to train state-of-the-art
recognition models for OCR software 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 from linguistically motivated
transcriptions.Comment: Submitted to JLCL Volume 33 (2018), Issue 1: Special Issue on
Automatic Text and Layout Recognitio
