Using SMT for OCR Error Correction of Historical Texts

Haithem Afli, Zhengwei Qiu, Andy Way, Páraic Sheridan

Abstract
A trend to digitize historical paper-based archives has emerged in recent years, with the advent of digital optical scanners. A lot of paper-based books, textbooks, magazines, articles, and documents are being transformed into electronic versions that can be manipulated by a computer. For this purpose, Optical Character Recognition (OCR) systems have been developed to transform scanned digital text into editable computer text. However, different kinds of errors in the OCR system output text can be found, but Automatic Error Correction tools can help in performing the quality of electronic texts by cleaning and removing noises. In this paper, we perform a qualitative and quantitative comparison of several error-correction techniques for historical French documents. Experimentation shows that our Machine Translation for Error Correction method is superior to other Language Modelling correction techniques, with nearly 13% relative improvement compared to the initial baseline.
Anthology ID:
L16-1153
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
962–966
Language:
URL:
https://aclanthology.org/L16-1153
DOI:
Bibkey:
Cite (ACL):
Haithem Afli, Zhengwei Qiu, Andy Way, and Páraic Sheridan. 2016. Using SMT for OCR Error Correction of Historical Texts. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 962–966, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Using SMT for OCR Error Correction of Historical Texts (Afli et al., LREC 2016)
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https://preview.aclanthology.org/ingest-acl-2023-videos/L16-1153.pdf
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