Lexically Aware Semi-Supervised Learning for OCR Post-Correction
Shruti Rijhwani, Daisy Rosenblum, Antonios Anastasopoulos, Graham Neubig
Abstract
Much of the existing linguistic data in many languages of the world is locked away in non- digitized books and documents. Optical character recognition (OCR) can be used to produce digitized text, and previous work has demonstrated the utility of neural post-correction methods that improve the results of general- purpose OCR systems on recognition of less- well-resourced languages. However, these methods rely on manually curated post- correction data, which are relatively scarce compared to the non-annotated raw images that need to be digitized. In this paper, we present a semi-supervised learning method that makes it possible to utilize these raw images to improve performance, specifically through the use of self-training, a technique where a model is iteratively trained on its own outputs. In addition, to enforce consistency in the recognized vocabulary, we introduce a lexically aware decoding method that augments the neural post-correction model with a count-based language model constructed from the recognized texts, implemented using weighted finite-state automata (WFSA) for efficient and effective decoding. Results on four endangered languages demonstrate the utility of the proposed method, with relative error reductions of 15%–29%, where we find the combination of self-training and lexically aware decoding essential for achieving consistent improvements.1- Anthology ID:
- 2021.tacl-1.76
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 9
- Month:
- Year:
- 2021
- Address:
- Cambridge, MA
- Editors:
- Brian Roark, Ani Nenkova
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 1285–1302
- Language:
- URL:
- https://aclanthology.org/2021.tacl-1.76
- DOI:
- 10.1162/tacl_a_00427
- Cite (ACL):
- Shruti Rijhwani, Daisy Rosenblum, Antonios Anastasopoulos, and Graham Neubig. 2021. Lexically Aware Semi-Supervised Learning for OCR Post-Correction. Transactions of the Association for Computational Linguistics, 9:1285–1302.
- Cite (Informal):
- Lexically Aware Semi-Supervised Learning for OCR Post-Correction (Rijhwani et al., TACL 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.tacl-1.76.pdf