OCR Improves Machine Translation for Low-Resource Languages

Oana Ignat, Jean Maillard, Vishrav Chaudhary, Francisco Guzmán


Abstract
We aim to investigate the performance of current OCR systems on low resource languages and low resource scripts.We introduce and make publicly available a novel benchmark, OCR4MT, consisting of real and synthetic data, enriched with noise, for 60 low-resource languages in low resource scripts. We evaluate state-of-the-art OCR systems on our benchmark and analyse most common errors. We show that OCR monolingual data is a valuable resource that can increase performance of Machine Translation models, when used in backtranslation. We then perform an ablation study to investigate how OCR errors impact Machine Translation performance and determine what is the minimum level of OCR quality needed for the monolingual data to be useful for Machine Translation.
Anthology ID:
2022.findings-acl.92
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1164–1174
Language:
URL:
https://aclanthology.org/2022.findings-acl.92
DOI:
10.18653/v1/2022.findings-acl.92
Bibkey:
Cite (ACL):
Oana Ignat, Jean Maillard, Vishrav Chaudhary, and Francisco Guzmán. 2022. OCR Improves Machine Translation for Low-Resource Languages. In Findings of the Association for Computational Linguistics: ACL 2022, pages 1164–1174, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
OCR Improves Machine Translation for Low-Resource Languages (Ignat et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.92.pdf