  In order to apply Optical Character Recognition (OCR) to historical printings
of Latin script fully automatically, we report on our efforts to construct a
widely-applicable polyfont recognition model yielding text with a Character
Error Rate (CER) around 2% when applied out-of-the-box. Moreover, we show how
this model can be further finetuned to specific classes of printings with
little manual and computational effort. The mixed or polyfont model is trained
on a wide variety of materials, in terms of age (from the 15th to the 19th
century), typography (various types of Fraktur and Antiqua), and languages
(among others, German, Latin, and French). To optimize the results we combined
established techniques of OCR training like pretraining, data augmentation, and
voting. In addition, we used various preprocessing methods to enrich the
training data and obtain more robust models. We also implemented a two-stage
approach which first trains on all available, considerably unbalanced data and
then refines the output by training on a selected more balanced subset.
Evaluations on 29 previously unseen books resulted in a CER of 1.73%,
outperforming a widely used standard model with a CER of 2.84% by almost 40%.
Training a more specialized model for some unseen Early Modern Latin books
starting from our mixed model led to a CER of 1.47%, an improvement of up to
50% compared to training from scratch and up to 30% compared to training from
the aforementioned standard model. Our new mixed model is made openly available
to the community.
