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

Mika Hämäläinen, Simon Hengchen


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.
Anthology ID:
R19-1051
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
431–436
Language:
URL:
https://aclanthology.org/R19-1051
DOI:
10.26615/978-954-452-056-4_051
Bibkey:
Cite (ACL):
Mika Hämäläinen and Simon Hengchen. 2019. From the Paft to the Fiiture: a Fully Automatic NMT and Word Embeddings Method for OCR Post-Correction. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 431–436, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
From the Paft to the Fiiture: a Fully Automatic NMT and Word Embeddings Method for OCR Post-Correction (Hämäläinen & Hengchen, RANLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/R19-1051.pdf
Code
 mikahama/natas