  In this paper we evaluate Optical Character Recognition (OCR) of 19th century
Fraktur scripts without book-specific training using mixed models, i.e. models
trained to recognize a variety of fonts and typesets from previously unseen
sources. We describe the training process leading to strong mixed OCR models
and compare them to freely available models of the popular open source engines
OCRopus and Tesseract as well as the commercial state of the art system ABBYY.
For evaluation, we use a varied collection of unseen data from books, journals,
and a dictionary from the 19th century. The experiments show that training
mixed models with real data is superior to training with synthetic data and
that the novel OCR engine Calamari outperforms the other engines considerably,
on average reducing ABBYYs character error rate (CER) by over 70%, resulting in
an average CER below 1%.
