Our evaluations showed that open source engines can outperform the commercial state-of-the-art system ABBYY by up to 78% if properly trained. The resulting models as well as the data required to adjust the model’s codec are publicly available <ref xmlns="http://www.tei-c.org/ns/1.0" target="https://github.com/chreul/19th-century-fraktur-OCR">(https://github.com/chreul/19th-century-fraktur-OCR)</ref>. Further improvements can be expected by providing more ground truth for training the mixed model and by using even deeper neural networks than the Calamari default. While ABBYY already has strong postprocessing techniques available, this represents an opportunity to improve the results achieved by Calamari and OCRopus even further, in particular the inclusion of dictionaries and language models.