Combining OCR Models for Reading Early Modern Printed Books
Mathias Seuret Janne van der Loop Nikolaus Weichselbaumer Martin Mayr Janina Molnar Tatjana Hass Florian Kordon Anguelos Nicolau Vincent Christlein
In this paper, we investigate the usage of fine-grained font recognition on
OCR for books printed from the 15th to the 18th century. We used a newly
created dataset for OCR of early printed books for which fonts are labeled with
bounding boxes. We know not only the font group used for each character, but
the locations of font changes as well. In books of this period, we frequently
find font group changes mid-line or even mid-word that indicate changes in
language. We consider 8 different font groups present in our corpus and
investigate 13 different subsets: the whole dataset and text lines with a
single font, multiple fonts, Roman fonts, Gothic fonts, and each of the
considered fonts, respectively. We show that OCR performance is strongly
impacted by font style and that selecting fine-tuned models with font group
recognition has a very positive impact on the results. Moreover, we developed a
system using local font group recognition in order to combine the output of
multiple font recognition models, and show that while slower, this approach
performs better not only on text lines composed of multiple fonts but on the
ones containing a single font only as well.