To achieve high quality results on early prints it is usually necessary to perform a book-specific training. For our 19<hi xmlns="http://www.tei-c.org/ns/1.0" rend="superscript">th</hi> century mixed model we try to avoid this by training on a wide variety of sources over four subsequent training steps (see Table 1). First, we use corpora with texts from different centuries for pretraining to achieve a certain overall robustness. Next, the training continues by incorporating synthetic data generated from freely available Fraktur fonts. The training concludes with the addition of real Fraktur data from the 19<hi xmlns="http://www.tei-c.org/ns/1.0" rend="superscript">th</hi> century. After training on the entire data set, we perform a final refinement step in which we only use a subset of at most 50 lines per book in order to prevent the model from overfitting to the books with a high number GT lines available (10,000+ compared to less than 50 for some books). The described data are mostly available online in the GT4HistOCR corpus (Springmann et al. 2018).