A striking result is the great variation among the CERs, e.g. by a factor of more than 2,500 from 26.54% to 0.01% for ABBYY and more than 400 from 4.75% to 0.01% for Calamari voted, which probably depends on the quality of the scans as well as the similarity of each font to the training data. Furthermore, training a model on real Fraktur data outperforms a model trained on mostly synthetic data generated for Fraktur (e.g. FRK vs. OCRo). The self-trained Calamari models achieve the best results, outperforming ABBYY by 70% without voting and even by 78% with voting averaged over all 20 datasets yielding an average CER below 1%.