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From the Paft to the Fiiture: a Fully Automatic NMT and Word Embeddings Method for OCR Post-Correction

2019-10-12 09:23:11
Mika Hämäläinen, Simon Hengchen
arXiv_CL
             
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/1910.05535

PDF

https://arxiv.org/pdf/1910.05535.pdf

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