PreP-OCR: A Complete Pipeline for Document Image Restoration and Enhanced OCR Accuracy

• URL: http://arxiv.org/abs/2505.20429v2
• Date: Wed, 28 May 2025 12:04:19 GMT
• Title: PreP-OCR: A Complete Pipeline for Document Image Restoration and Enhanced OCR Accuracy
• Authors: Shuhao Guan, Moule Lin, Cheng Xu, Xinyi Liu, Jinman Zhao, Jiexin Fan, Qi Xu, Derek Greene,
• Abstract summary: PreP-OCR is a two-stage pipeline that combines document image restoration with semantic-aware post-OCR correction.<n>We show that PreP-OCR reduces character error rates by 63.9-70.3% compared to OCR on raw images.
• Score: 14.50674472785442
• License: http://creativecommons.org/licenses/by/4.0/
• 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correction to enhance both visual clarity and textual consistency, thereby improving text extraction from degraded historical documents.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 Detailed experiments on 13,831 pages of real historical documents in English, French, and Spanish show that the PreP-OCR pipeline reduces character error rates by 63.9-70.3% compared to OCR on raw images.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.

Related papers

• Recycling the Web: A Method to Enhance Pre-training Data Quality and Quantity for Language Models [107.24906866038431]
  We propose REWIRE, REcycling the Web with guIded REwrite, to enrich low-quality documents so that they could become useful for training.<n>We show that mixing high-quality raw texts and our rewritten texts lead to 1.0, 1.3 and 2.5 percentage points improvement respectively across 22 diverse tasks.
  arXiv  Detail & Related papers  (2025-06-05T07:12:12Z)
• TFIC: End-to-End Text-Focused Image Compression for Coding for Machines [50.86328069558113]
  We present an image compression system designed to retain text-specific features for subsequent Optical Character Recognition (OCR)<n>Our encoding process requires half the time needed by the OCR module, making it especially suitable for devices with limited computational capacity.
  arXiv  Detail & Related papers  (2025-03-25T09:36:13Z)
• KAP: MLLM-assisted OCR Text Enhancement for Hybrid Retrieval in Chinese Non-Narrative Documents [0.0]
  We propose Knowledge-Aware Preprocessing (KAP), a novel framework that transforms noisy OCR outputs into retrieval-optimized text.<n>KAP adopts a two-stage approach: it first extracts text using OCR, then employs Multimodal Large Language Models to refine the output.<n> Empirical results demonstrate that KAP consistently and significantly outperforms conventional preprocessing approaches.
  arXiv  Detail & Related papers  (2025-03-11T14:01:03Z)
• RoundTripOCR: A Data Generation Technique for Enhancing Post-OCR Error Correction in Low-Resource Devanagari Languages [41.09752906121257]
  We propose an approach for synthetic data generation for Devanagari languages, RoundTripOCR.<n>We release post-OCR text correction datasets for Hindi, Marathi, Bodo, Nepali, Konkani and Sanskrit.<n>We also present a novel approach for OCR error correction by leveraging techniques from machine translation.
  arXiv  Detail & Related papers  (2024-12-14T19:59:41Z)
• Reference-Based Post-OCR Processing with LLM for Precise Diacritic Text in Historical Document Recognition [1.6941039309214678]
  We propose a method utilizing available content-focused ebooks as a reference base to correct imperfect OCR-generated text.<n>This technique generates high-precision pseudo-page-to-page labels for diacritic languages.<n>The pipeline eliminates various types of noise from aged documents and addresses issues such as missing characters, words, and disordered sequences.
  arXiv  Detail & Related papers  (2024-10-17T08:05:02Z)
• Post-OCR Text Correction for Bulgarian Historical Documents [31.072768715994318]
  We create the first benchmark dataset for evaluating the OCR text correction for historical Bulgarian documents written in the first standardized Bulgarian orthography: the Drinov orthography from the 19th century. We then use state-of-the-art LLMs and encoder-decoder framework which we augment with diagonal attention loss and copy and coverage mechanisms to improve the post-OCR text correction. The proposed method reduces the errors introduced during recognition and improves the quality of the documents by 25%, which is an increase of 16% compared to the state-of-the-art on the ICDAR 2019
  arXiv  Detail & Related papers  (2024-08-31T19:27:46Z)
• Decoder Pre-Training with only Text for Scene Text Recognition [54.93037783663204]
  Scene text recognition (STR) pre-training methods have achieved remarkable progress, primarily relying on synthetic datasets. We introduce a novel method named Decoder Pre-training with only text for STR (DPTR) DPTR treats text embeddings produced by the CLIP text encoder as pseudo visual embeddings and uses them to pre-train the decoder.
  arXiv  Detail & Related papers  (2024-08-11T06:36:42Z)
• Image Super-Resolution with Text Prompt Diffusion [118.023531454099]
  We introduce text prompts to image SR to provide degradation priors.<n>PromptSR leverages the latest multi-modal large language model (MLLM) to generate prompts from low-resolution images.<n>Experiments indicate that introducing text prompts into SR, yields impressive results on both synthetic and real-world images.
  arXiv  Detail & Related papers  (2023-11-24T05:11:35Z)
• PHD: Pixel-Based Language Modeling of Historical Documents [55.75201940642297]
  We propose a novel method for generating synthetic scans to resemble real historical documents. We pre-train our model, PHD, on a combination of synthetic scans and real historical newspapers from the 1700-1900 period. We successfully apply our model to a historical QA task, highlighting its usefulness in this domain.
  arXiv  Detail & Related papers  (2023-10-22T08:45:48Z)
• DISGO: Automatic End-to-End Evaluation for Scene Text OCR [16.231114992450895]
  We propose to uniformly use word error rates (WER) as a new measurement for evaluating scene-text OCR. Particularly for the e2e metric, we name it DISGO WER as it considers Deletion, Insertion, Substitution, and Grouping/Ordering errors.
  arXiv  Detail & Related papers  (2023-08-25T04:45:37Z)
• TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models [47.48019831416665]
  We propose an end-to-end text recognition approach with pre-trained image Transformer and text Transformer models, namely TrOCR. TrOCR is simple but effective, and can be pre-trained with large-scale synthetic data and fine-tuned with human-labeled datasets. Experiments show that the TrOCR model outperforms the current state-of-the-art models on both printed and handwritten text recognition tasks.
  arXiv  Detail & Related papers  (2021-09-21T16:01:56Z)
• Lights, Camera, Action! A Framework to Improve NLP Accuracy over OCR documents [2.6201102730518606]
  We demonstrate an effective framework for mitigating OCR errors for any downstream NLP task. We first address the data scarcity problem for model training by constructing a document synthesis pipeline. For the benefit of the community, we have made the document synthesis pipeline available as an open-source project.
  arXiv  Detail & Related papers  (2021-08-06T00:32:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.