  A great deal of historical corpora suffer from errors introduced by the OCR
(optical character recognition) methods used in the digitization process.
Correcting these errors manually is a time-consuming process and a great part
of the automatic approaches have been relying on rules or supervised machine
learning. We present a fully automatic unsupervised way of extracting parallel
data for training a character-based sequence-to-sequence NMT (neural machine
translation) model to conduct OCR error correction.
