Combining OCR Models for Reading Early Modern Printed Books
This work addresses OCR challenges for historical documents, offering incremental improvements for digitization efforts in libraries and archives.
The paper tackled OCR for early modern printed books by using fine-grained font recognition to improve accuracy, showing that selecting fine-tuned models based on font groups significantly boosts performance, with a system combining multiple models performing better even on single-font text lines.
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.