Abstract: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 combin… Show more
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