  In this paper, we propose a novel method based on character
sequence-to-sequence models to correct documents already processed with Optical
Character Recognition (OCR) systems. The main contribution of this paper is a
set of strategies to accurately process strings much longer than the ones used
to train the sequence model while being sample- and resource-efficient,
supported by thorough experimentation. The strategy with the best performance
involves splitting the input document in character n-grams and combining their
individual corrections into the final output using a voting scheme that is
equivalent to an ensemble of a large number of sequence models. We further
investigate how to weigh the contributions from each one of the members of this
ensemble. We test our method on nine languages of the ICDAR 2019 competition on
post-OCR text correction and achieve a new state-of-the-art performance in five
of them. Our code for post-OCR correction is shared at
https://github.com/jarobyte91/post_ocr_correction.
