  Good OCR results for historical printings rely on the availability of
recognition models trained on diplomatic transcriptions as ground truth, which
is both a scarce resource and time-consuming to generate. Instead of having to
train a separate model for each historical typeface, we propose a strategy to
start from models trained on a combined set of available transcriptions in a
variety of fonts. These \emph{mixed models} result in character accuracy rates
over 90\% on a test set of printings from the same period of time, but without
any representation in the training data, demonstrating the possibility to
overcome the typography barrier by generalizing from a few typefaces to a
larger set of (similar) fonts in use over a period of time. The output of these
mixed models is then used as a baseline to be further improved by both fully
automatic methods and semi-automatic methods involving a minimal amount of
manual transcriptions. In order to evaluate the recognition quality of each
model in a series of models generated during the training process in the
absence of any ground truth, we introduce two readily observable quantities
that correlate well with true accuracy. These quantities are \emph{mean
character confidence C} (as given by the OCR engine OCRopus) and \emph{mean
token lexicality L} (a distance measure of OCR tokens from modern wordforms
taking historical spelling patterns into account, which can be calculated for
any OCR engine). Whereas the fully automatic method is able to improve upon the
result of a mixed model by only 1-2 percentage points, already 100-200
hand-corrected lines lead to much better OCR results with character error rates
of only a few percent. This procedure minimizes the amount of ground truth
production and does not depend on the previous construction of a specific
typographic model.
