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.
@article{arxiv.1910.05535,
title = {From the Paft to the Fiiture: a Fully Automatic NMT and Word Embeddings Method for OCR Post-Correction},
author = {Mika Hämäläinen and Simon Hengchen},
journal= {arXiv preprint arXiv:1910.05535},
year = {2020}
}