# OCR Error Post-Correction with LLMs in Historical Documents: No Free Lunches

arXiv: https://arxiv.org/abs/2502.01205 · HTML: https://mlsanity.com/paper/2502.01205/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 3 Feb 2025.

## Builds on (most cited first, 5 of 5)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Poro 34B and the Blessing of Multilinguality](https://mlsanity.com/paper/2404.01856/) (2024)
- [An Unsupervised method for OCR Post-Correction and Spelling Normalisation for Finnish](https://mlsanity.com/paper/2011.03502/) (2020)
- [Evaluating GPT-3.5 and GPT-4 on Grammatical Error Correction for Brazilian Portuguese](https://mlsanity.com/paper/2306.15788/) (2023)
- [Post-OCR Text Correction for Bulgarian Historical Documents](https://mlsanity.com/paper/2409.00527/) (2024)

## Built on by (most cited first, 11 of 11)

- [Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documents](https://mlsanity.com/paper/2504.00414/) (2025)
- [Long-Tail Knowledge in Large Language Models: Taxonomy, Mechanisms, Interventions and Implications](https://mlsanity.com/paper/2602.16201/) (2026)
- [Reading the unreadable: Creating a dataset of 19th century English newspapers using image-to-text language models](https://mlsanity.com/paper/2502.14901/) (2025)
- [The German Commons - 154 Billion Tokens of Openly Licensed Text for German Language Models](https://mlsanity.com/paper/2510.13996/) (2025)
- [Detecting Latin in Historical Books with Large Language Models: A Multimodal Benchmark](https://mlsanity.com/paper/2510.19585/) (2025)
- [Quid est VERITAS? A Modular Framework for Archival Document Analysis](https://mlsanity.com/paper/2603.28108/) (2026)
- [The Devil is in the Details -- From OCR for Old Church Slavonic to Purely Visual Stemma Reconstruction](https://mlsanity.com/paper/2604.11724/) (2026)
- [ICDAR 2026 HIPE-OCRepair Competition on LLM-Assisted OCR Post-Correction for Historical Documents](https://mlsanity.com/paper/2607.08143/) (2026)
- [When Do VLMs Help Arabic Manuscript OCR? A Cross-Dataset Study](https://mlsanity.com/paper/2608.22366/) (2026)
- [Evaluating In-Context Learning and Retrieval Strategies for Devanagari Post-OCR Correction](https://mlsanity.com/paper/2609.21595/) (2026)
- [DDVR-Net: Degradation-Aware Decoupling and Visibility-Guided Restoration for Historical Document Recognition](https://mlsanity.com/paper/2610.16956/) (2026)

## Abstract (arXiv)

Optical Character Recognition (OCR) systems often introduce errors when transcribing historical documents, leaving room for post-correction to improve text quality. This study evaluates the use of open-weight LLMs for OCR error correction in historical English and Finnish datasets. We explore various strategies, including parameter optimization, quantization, segment length effects, and text continuation methods. Our results demonstrate that while modern LLMs show promise in reducing character error rates (CER) in English, a practically useful performance for Finnish was not reached. Our findings highlight the potential and limitations of LLMs in scaling OCR post-correction for large historical corpora.