During the last few years, great progress has been made on OCR methods which can mainly be attributed to the introduction of a line-based recognition approach using recurrent neural networks (Breuel et al. 2013). Since this breakthrough, impressive recognition accuracies beyond 98% have been achieved on a variety of materials, ranging from the earliest printed books (Springmann et al. 2016; Springmann and Lüdeling 2017) to modern prints (Breuel 2017; Wick et al. 2018). Early prints show a high variability in terms of printing types and therefore usually require book-specific training in order to reach desirable character error rates (CER) below 1-2%. On the contrary, modern typography is much more regular and mixed models, i.e. models trained on a variety of fonts and typesets from different sources, comfortably achieve CERs well below 1% without any book-specific training. Apart from the aforementioned introduction of new recognition techniques and network structures, several methodical improvements like pretraining (transfer learning) and majority or confidence voting have been introduced and successfully evaluated, especially for the application on early printed books (Reul et al. 2018).