# Reference-Based Post-OCR Processing with LLM for Precise Diacritic Text in Historical Document Recognition

arXiv: https://arxiv.org/abs/2410.13305 · HTML: https://mlsanity.com/paper/2410.13305/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 17 Oct 2024.

## Builds on (most cited first, 9 of 9)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [YOLO-World: Real-Time Open-Vocabulary Object Detection](https://mlsanity.com/paper/2401.17270/) (2024)
- [LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis](https://mlsanity.com/paper/2103.15348/) (2021)
- [ViTextVQA: A Large-Scale Visual Question Answering Dataset and a Novel Multimodal Feature Fusion Method for Vietnamese Text Comprehension in Images](https://mlsanity.com/paper/2404.10652/) (2024)
- [VSEC: Transformer-based Model for Vietnamese Spelling Correction](https://mlsanity.com/paper/2111.00640/) (2021)
- [Hierarchical Transformer Encoders for Vietnamese Spelling Correction](https://mlsanity.com/paper/2105.13578/) (2021)

## Built on by (most cited first, 6 of 6)

- [CHURRO: Making History Readable with an Open-Weight Large Vision-Language Model for High-Accuracy, Low-Cost Historical Text Recognition](https://mlsanity.com/paper/2509.19768/) (2025)
- [SAT: Selective Aggregation Transformer for Image Super-Resolution](https://mlsanity.com/paper/2604.07994/) (2026)
- [RefLAM: A Reference-Grounded Line Annotation Pipeline for Historical Arabic Manuscripts](https://mlsanity.com/paper/2608.25140/) (2026)
- [Direct Image-to-Modern Vietnamese Translation of Han-Nom Manuscripts via Multimodal RLHF Preference Alignment](https://mlsanity.com/paper/2607.11434/) (2026)
- [When Do VLMs Help Arabic Manuscript OCR? A Cross-Dataset Study](https://mlsanity.com/paper/2608.22366/) (2026)
- [Equal CER, Unequal Damage: Severity-Weighted Character Error Rate for OCR](https://mlsanity.com/paper/2610.20461/) (2026)

## Abstract (arXiv)

Extracting fine-grained OCR text from aged documents in diacritic languages remains challenging due to unexpected artifacts, time-induced degradation, and lack of datasets. While standalone spell correction approaches have been proposed, they show limited performance for historical documents due to numerous possible OCR error combinations and differences between modern and classical corpus distributions. We propose a method utilizing available content-focused ebooks as a reference base to correct imperfect OCR-generated text, supported by large language models. This technique generates high-precision pseudo-page-to-page labels for diacritic languages, where small strokes pose significant challenges in historical conditions. The pipeline eliminates various types of noise from aged documents and addresses issues such as missing characters, words, and disordered sequences. Our post-processing method, which generated a large OCR dataset of classical Vietnamese books, achieved a mean grading score of 8.72 on a 10-point scale. This outperformed the state-of-the-art transformer-based Vietnamese spell correction model, which scored 7.03 when evaluated on a sampled subset of the dataset. We also trained a baseline OCR model to assess and compare it with well-known engines. Experimental results demonstrate the strength of our baseline model compared to widely used open-source solutions. The resulting dataset will be released publicly to support future studies.