  This paper deals with the task of practical and open source Handwritten Text
Recognition (HTR) on German medieval manuscripts. We report on our efforts to
construct mixed recognition models which can be applied out-of-the-box without
any further document-specific training but also serve as a starting point for
finetuning by training a new model on a few pages of transcribed text (ground
truth). To train the mixed models we collected a corpus of 35 manuscripts and
ca. 12.5k text lines for two widely used handwriting styles, Gothic and
Bastarda cursives. Evaluating the mixed models out-of-the-box on four unseen
manuscripts resulted in an average Character Error Rate (CER) of 6.22%. After
training on 2, 4 and eventually 32 pages the CER dropped to 3.27%, 2.58%, and
1.65%, respectively. While the in-domain recognition and training of models
(Bastarda model to Bastarda material, Gothic to Gothic) unsurprisingly yielded
the best results, finetuning out-of-domain models to unseen scripts was still
shown to be superior to training from scratch.
  Our new mixed models have been made openly available to the community.
