Revise: A Framework for Revising OCRed text in Practical Information Systems with Data Contamination Strategy

Gyuho Shim; Heuiseok Lim; Seongtae Hong

arxiv: 2604.08115 · v1 · submitted 2026-04-09 · 💻 cs.AI

Revise: A Framework for Revising OCRed text in Practical Information Systems with Data Contamination Strategy

Gyuho Shim , Seongtae Hong , Heuiseok Lim This is my paper

Pith reviewed 2026-05-10 17:24 UTC · model grok-4.3

classification 💻 cs.AI

keywords OCR error correctiondocument AIhierarchical taxonomysynthetic data generationtext revisiondocument retrievalquestion answering

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The pith

Revise corrects OCR errors at character, word, and structural levels using a hierarchical taxonomy and synthetic data generation to enable structured document management and improved retrieval and question answering.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Revise to fix the structural management gap in current Document AI systems that process OCRed text. It builds a detailed taxonomy covering common error types and creates synthetic training data that mimics real OCR mistakes to train a correction model. This matters to a sympathetic reader because cleaner, better-organized documents would make information retrieval and question answering more reliable in practical settings. The approach focuses on turning error-prone OCR output into systematically manageable content rather than tackling isolated tasks. If the method works, it provides a preprocessing layer that supports broader document organization.

Core claim

Revise employs a comprehensive hierarchical taxonomy of common OCR errors and a synthetic data generation strategy that realistically simulates such errors to train an effective correction model. Experimental results demonstrate that Revise effectively corrects OCR outputs, enabling more structured representation and systematic management of document contents. Consequently, our method significantly enhances downstream performance in document retrieval and question answering tasks.

What carries the argument

The Revise framework, built around a hierarchical taxonomy of OCR errors combined with a data contamination strategy that generates synthetic training examples to train a multi-level correction model.

Load-bearing premise

The synthetic data generation strategy realistically simulates actual OCR errors and the hierarchical taxonomy comprehensively covers common error types in practical documents.

What would settle it

Applying the trained Revise model to a held-out collection of real OCRed documents from a standard benchmark and finding no measurable improvement in retrieval accuracy or QA performance over the uncorrected baseline would falsify the claim.

Figures

Figures reproduced from arXiv: 2604.08115 by Gyuho Shim, Heuiseok Lim, Seongtae Hong.

read the original abstract

Recent advances in Large Language Models (LLMs) have significantly improved the field of Document AI, demonstrating remarkable performance on document understanding tasks such as question answering. However, existing approaches primarily focus on solving specific tasks, lacking the capability to structurally organize and manage document information. To address this limitation, we propose Revise, a framework that systematically corrects errors introduced by OCR at the character, word, and structural levels. Specifically, Revise employs a comprehensive hierarchical taxonomy of common OCR errors and a synthetic data generation strategy that realistically simulates such errors to train an effective correction model. Experimental results demonstrate that Revise effectively corrects OCR outputs, enabling more structured representation and systematic management of document contents. Consequently, our method significantly enhances downstream performance in document retrieval and question answering tasks, highlighting the potential to overcome the structural management limitations of existing Document AI frameworks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Revise adds a hierarchical OCR error taxonomy plus synthetic contamination for training correction models, but the gains look tied to synthetic data that may not match real scanned documents.

read the letter

The paper's main move is to define a three-level taxonomy of OCR errors (character, word, structural) and then use a synthetic contamination process to generate training data for an LLM-based corrector. That combination is presented as a way to produce cleaner document representations that feed better into retrieval and QA pipelines in practical Document AI setups. The abstract frames this as addressing the gap where current systems handle isolated tasks but do not systematically clean and organize OCR output first. The taxonomy itself is a reasonable organizing device that could help practitioners catalog common failure modes without starting from scratch. The synthetic generation step is also a direct response to the usual shortage of paired clean/noisy OCR data. Both pieces are described clearly enough that someone working on post-processing pipelines could pick them up and try them. The central claim is that the resulting corrector improves downstream retrieval and QA. That direction is sensible given how OCR noise propagates. The soft spot is the evaluation. The abstract mentions experimental results but supplies no baselines, no metric definitions, no error bars, and no indication that the test set came from independent real OCR runs on scanned documents rather than more data generated by the same contamination process. When training and test distributions are built the same way, measured gains can reflect distribution matching instead of genuine robustness. The stress-test note is right on this point: without held-out real-world validation, the practical claims rest on weaker ground. This work is for applied researchers and engineers who already run document pipelines and need a structured way to reduce OCR noise before feeding data into retrieval or QA components. A reader who cares about incremental engineering improvements in Document AI will find the taxonomy and data strategy worth looking at. It is coherent on its own terms and engages the literature enough to merit referee time, even though the experiments will require substantial clarification on data sources and controls. I would send it for review rather than desk reject.

Referee Report

2 major / 1 minor

Summary. The paper proposes Revise, a framework for systematically correcting OCR errors in documents at character, word, and structural levels. It introduces a hierarchical taxonomy of common OCR errors combined with a synthetic data generation strategy (data contamination) to train a correction model. The central claim is that this enables more structured representation of document contents and yields significant improvements in downstream document retrieval and question answering tasks.

Significance. If the experimental claims hold under independent real-world validation, Revise could provide a practical preprocessing step for Document AI systems, addressing a persistent bottleneck in OCR-based pipelines and improving reliability of retrieval and QA over noisy document collections. The hierarchical taxonomy and contamination approach represent a structured attempt to model error patterns, which is a positive direction if shown to generalize beyond synthetic data.

major comments (2)

[Abstract / Experimental Results] Abstract and Experimental Results section: The claim that Revise 'significantly enhances downstream performance in document retrieval and question answering tasks' is presented without any reported baselines, evaluation metrics, statistical details (error bars, significance tests), dataset sizes, or ablation studies. This absence directly undermines assessment of the central empirical claim.
[Methodology / Experimental Results] Methodology and Experimental Results sections: The synthetic data generation strategy is asserted to 'realistically simulate' OCR errors, yet no validation is described comparing the generated error distributions against a held-out set of real OCR outputs from scanned documents. If test sets are also synthetically contaminated using the same taxonomy, performance gains may reflect distribution matching rather than robust correction of practical OCR noise.

minor comments (1)

[Abstract] The abstract and introduction would benefit from explicit statements on the scope of the hierarchical taxonomy (e.g., which languages or document types it covers) to clarify generalizability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful and constructive feedback. We agree that the current manuscript presentation lacks sufficient experimental details and validation steps, which weakens the central claims. We will revise the paper to address both major comments directly by expanding the methodology and results sections with the requested information.

read point-by-point responses

Referee: [Abstract / Experimental Results] Abstract and Experimental Results section: The claim that Revise 'significantly enhances downstream performance in document retrieval and question answering tasks' is presented without any reported baselines, evaluation metrics, statistical details (error bars, significance tests), dataset sizes, or ablation studies. This absence directly undermines assessment of the central empirical claim.

Authors: We acknowledge this is a valid criticism of the current draft. The manuscript as submitted does not report the specific baselines (e.g., standard OCR post-correction tools or LLM-based correctors), evaluation metrics (such as character error rate, word error rate, or downstream retrieval/QA accuracy), statistical details, dataset sizes, or ablation studies. In the revised version we will add a dedicated experimental subsection that includes: (1) explicit baselines and comparisons, (2) quantitative metrics with error bars from multiple runs and significance tests, (3) dataset sizes and splits, and (4) ablation results isolating the contribution of the hierarchical taxonomy and contamination strategy. These additions will be placed in both the Experimental Results section and referenced in the abstract. revision: yes
Referee: [Methodology / Experimental Results] Methodology and Experimental Results sections: The synthetic data generation strategy is asserted to 'realistically simulate' OCR errors, yet no validation is described comparing the generated error distributions against a held-out set of real OCR outputs from scanned documents. If test sets are also synthetically contaminated using the same taxonomy, performance gains may reflect distribution matching rather than robust correction of practical OCR noise.

Authors: We agree that explicit validation of the synthetic error distribution against real OCR outputs is necessary to support the claim of realism. The current manuscript does not include such a comparison. In the revision we will add: (1) a quantitative comparison (e.g., error-type frequency histograms) between our contaminated synthetic data and a held-out set of real OCR outputs from scanned documents, and (2) clarification that while training and some evaluation use the contamination strategy, we will also report results on real OCRed documents from public corpora. This will help demonstrate that gains are not solely due to distribution matching. We will update the Methodology section accordingly. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper defines Revise using an external hierarchical taxonomy of common OCR errors and a synthetic data generation strategy presented as realistic simulation of those errors. These serve as inputs to train a correction model, with experimental results reported as empirical outcomes on downstream retrieval and QA tasks. No equation, claim, or step reduces the performance gains to a self-definition, fitted parameter renamed as prediction, or self-citation chain; the central improvements are treated as consequences of the trained model rather than tautological restatements of the taxonomy or generation process.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim depends on two key domain assumptions: that OCR errors follow patterns that can be exhaustively captured by a hierarchical taxonomy and that synthetic contamination can produce training data sufficiently close to real-world OCR outputs to yield effective correction models.

axioms (2)

domain assumption OCR errors occur in identifiable patterns at character, word, and structural levels that can be organized into a comprehensive hierarchical taxonomy.
Invoked to enable systematic correction across multiple levels.
domain assumption Synthetic data generated by contaminating clean text can realistically simulate real OCR errors for training purposes.
Central to the data generation strategy described in the abstract.

pith-pipeline@v0.9.0 · 5450 in / 1356 out tokens · 47910 ms · 2026-05-10T17:24:30.179769+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

8 extracted references · 8 canonical work pages

[1]

Jaume, H

Icdar2019 competition on scanned receipt ocr and information extraction. In2019 International Conference on Document Analysis and Recognition (ICDAR), pages 1516–1520. Guillaume Jaume, Hazim Kemal Ekenel, and Jean- Philippe Thiran. 2019. Funsd: A dataset for form understanding in noisy scanned documents.Preprint, arXiv:1905.13538. Harshvivek Kashid and Pu...

work page arXiv 2019
[2]

Publaynet: largest dataset ever for document layout analysis.Preprint, arXiv:1908.07836. A Contamination Strategy For our synthetic data contamination process, we carefully calibrated error ratios based on empiri- cal observations of real-world OCR outputs from a range of document types, spanning from well- structured documents to semi-structured document...

work page arXiv 1908
[3]

Substitution: Correct misread characters (e.g., ’I’ read as ’1’)

work page
[4]

Insertion: Remove unintentionally included characters or spaces

work page
[5]

Deletion: Restore omitted characters or words

work page
[6]

Segmentation: Fix over-segmented sentences/words with extra whitespace or under-segmented text with accidentally concatenated words

work page
[7]

Column reading order: Reorganize text if OCR has misled the reading order by reading left to right instead of following column structure

work page
[8]

If you think they should be retained, do not correct them

Take extra care with numeric values, dates, and proper nouns. If you think they should be retained, do not correct them. Additionally: - Retain Upper case and Lower case. - Remove unnecessary whitespace. - Mark unclear parts with ’[. . . ]’. - Retain personal information unless explicitly asked to remove it. - Correct typos, grammar, spacing, and punctuat...

work page

[1] [1]

Jaume, H

Icdar2019 competition on scanned receipt ocr and information extraction. In2019 International Conference on Document Analysis and Recognition (ICDAR), pages 1516–1520. Guillaume Jaume, Hazim Kemal Ekenel, and Jean- Philippe Thiran. 2019. Funsd: A dataset for form understanding in noisy scanned documents.Preprint, arXiv:1905.13538. Harshvivek Kashid and Pu...

work page arXiv 2019

[2] [2]

Publaynet: largest dataset ever for document layout analysis.Preprint, arXiv:1908.07836. A Contamination Strategy For our synthetic data contamination process, we carefully calibrated error ratios based on empiri- cal observations of real-world OCR outputs from a range of document types, spanning from well- structured documents to semi-structured document...

work page arXiv 1908

[3] [3]

Substitution: Correct misread characters (e.g., ’I’ read as ’1’)

work page

[4] [4]

Insertion: Remove unintentionally included characters or spaces

work page

[5] [5]

Deletion: Restore omitted characters or words

work page

[6] [6]

Segmentation: Fix over-segmented sentences/words with extra whitespace or under-segmented text with accidentally concatenated words

work page

[7] [7]

Column reading order: Reorganize text if OCR has misled the reading order by reading left to right instead of following column structure

work page

[8] [8]

If you think they should be retained, do not correct them

Take extra care with numeric values, dates, and proper nouns. If you think they should be retained, do not correct them. Additionally: - Retain Upper case and Lower case. - Remove unnecessary whitespace. - Mark unclear parts with ’[. . . ]’. - Retain personal information unless explicitly asked to remove it. - Correct typos, grammar, spacing, and punctuat...

work page