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arXiv · 2505.20429

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
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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correction to enhance both visual clarity and textual consistency, thereby improving text extraction from degraded historical documents.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 Detailed experiments on 13,831 pages of real historical documents in English, French, and Spanish show that the PreP-OCR pipeline reduces character error rates by 63.9-70.3% compared to OCR on raw images.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.

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Shuhao Guan, Moule Lin, Cheng Xu, Xinyi Liu, Jinman Zhao, Jiexin Fan, Qi Xu, Derek Greene. 2025-05-28. PreP-OCR: A Complete Pipeline for Document Image Restoration and Enhanced OCR Accuracy. https://doi.org/10.18653/v1%2F2025.acl-long.749

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