Unsupervised Multi-View Post-OCR Error Correction With Language Models

Harsh Gupta, Luciano Del Corro, Samuel Broscheit, Johannes Hoffart, Eliot Brenner


Abstract
We investigate post-OCR correction in a setting where we have access to different OCR views of the same document. The goal of this study is to understand if a pretrained language model (LM) can be used in an unsupervised way to reconcile the different OCR views such that their combination contains fewer errors than each individual view. This approach is motivated by scenarios in which unconstrained text generation for error correction is too risky. We evaluated different pretrained LMs on two datasets and found significant gains in realistic scenarios with up to 15% WER improvement over the best OCR view. We also show the importance of domain adaptation for post-OCR correction on out-of-domain documents.
Anthology ID:
2021.emnlp-main.680
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8647–8652
Language:
URL:
https://aclanthology.org/2021.emnlp-main.680
DOI:
10.18653/v1/2021.emnlp-main.680
Bibkey:
Cite (ACL):
Harsh Gupta, Luciano Del Corro, Samuel Broscheit, Johannes Hoffart, and Eliot Brenner. 2021. Unsupervised Multi-View Post-OCR Error Correction With Language Models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8647–8652, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Unsupervised Multi-View Post-OCR Error Correction With Language Models (Gupta et al., EMNLP 2021)
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PDF:
https://aclanthology.org/2021.emnlp-main.680.pdf