PreP-OCR: A Complete Pipeline for Document Image Restoration and Enhanced OCR Accuracy
Shuhao Guan, Moule Lin, Cheng Xu, Xinyi Liu, Jinman Zhao, Jiexin Fan, Qi Xu, Derek Greene
Abstract
This paper introduces PreP-OCR, a two-stage pipeline that combines document image restoration with semantic-aware post-OCR correction to enhance both visual clarity and textual consistency, thereby improving text extraction from degraded historical documents.First, we synthesize document-image pairs from plaintext, rendering them with diverse fonts and layouts and then applying a randomly ordered set of degradation operations. An image restoration model is trained on this synthetic data, using multi-directional patch extraction and fusion to process large images. Second, a ByT5 post-OCR model, fine-tuned on synthetic historical text pairs, addresses remaining OCR errors.Detailed experiments on 13,831 pages of real historical documents in English, French, and Spanish show that the PreP-OCR pipeline reduces character error rates by 63.9-70.3% compared to OCR on raw images. Our pipeline demonstrates the potential of integrating image restoration with linguistic error correction for digitizing historical archives.
BibTeX
@inproceedings{guan-etal-2025-prep,
title = "{P}re{P}-{OCR}: A Complete Pipeline for Document Image Restoration and Enhanced {OCR} Accuracy",
author = "Guan, Shuhao and
Lin, Moule and
Xu, Cheng and
Liu, Xinyi and
Zhao, Jinman and
Fan, Jiexin and
Xu, Qi and
Greene, Derek",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.749/",
doi = "10.18653/v1/2025.acl-long.749",
pages = "15413--15425",
ISBN = "979-8-89176-251-0"
}