  Identifying the type of font (e.g., Roman, Blackletter) used in historical
documents can help optical character recognition (OCR) systems produce more
accurate text transcriptions. Towards this end, we present an active-learning
strategy that can significantly reduce the number of labeled samples needed to
train a font classifier. Our approach extracts image-based features that
exploit geometric differences between fonts at the word level, and combines
them into a bag-of-word representation for each page in a document. We evaluate
six sampling strategies based on uncertainty, dissimilarity and diversity
criteria, and test them on a database containing over 3,000 historical
documents with Blackletter, Roman and Mixed fonts. Our results show that a
combination of uncertainty and diversity achieves the highest predictive
accuracy (89% of test cases correctly classified) while requiring only a small
fraction of the data (17%) to be labeled. We discuss the implications of this
result for mass digitization projects of historical documents.
