Abstract
In this paper we present a novel approach to the automatic correction of OCR-induced orthographic errors in a given text. While current systems depend heavily on large training corpora or external information, such as domain-specific lexicons or confidence scores from the OCR process, our system only requires a small amount of (relatively) clean training data from a representative corpus to learn a character-based statistical language model using Bidirectional Long Short-Term Memory Networks (biLSTMs). We demonstrate the versatility and adaptability of our system on different text corpora with varying degrees of textual noise, including a real-life OCR corpus in the medical domain.- Anthology ID:
- I17-1101
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Greg Kondrak, Taro Watanabe
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 1006–1014
- Language:
- URL:
- https://aclanthology.org/I17-1101
- DOI:
- Cite (ACL):
- Eva D’hondt, Cyril Grouin, and Brigitte Grau. 2017. Generating a Training Corpus for OCR Post-Correction Using Encoder-Decoder Model. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1006–1014, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- Generating a Training Corpus for OCR Post-Correction Using Encoder-Decoder Model (D’hondt et al., IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/I17-1101.pdf