Post-correction of Historical Text Transcripts with Large Language Models

An Exploratory Study (2023)

• Overview
• Repository Organisation
• Requirements
• Models
• Datasets
• Experiments
• Publication
• Follow-up Work: ICDAR 2026 HIPE-OCRepair Competition
• About

Overview

The quality of transcriptions of heritage documents produced by optical character recognition (OCR, for printed documents), handwritten text recognition (HTR, for manuscripts) or automatic speech recognition (ASR, for audio documents) has a major impact on the ability to search and process historical texts. This is the implementation of Post-correction of Historical Text Transcripts with Large Language Models: An Exploratory Study.

Can LLMs amend poor historical transcriptions?

• Ability to correct. Do LLMs improve, degrade, or leave the input text intact?
• Sensitivity to variations in input text and instructions. Does LLM post-correction performance depend on the noise of the original document? How sensitive is it to prompt instructions?
• Real-world applicability. How do open-access models compare with the limited-access ones? Could millions of noisy historical documents be easily corrected?

Repository organisation

llm-transcript-postcorrection/
├── data/
│   ├── datasets/
│   │   └── ina/*txt,*xml
│   └── prompts/
│   └── config.yml
├── lib/
│   ├── converters/
│   └── modules/
└── notebooks/
    └── ResultsGeneration.ipynb

• lib: main codebase for the experiments. Details here.
• notebooks: Jupyter notebooks for data error analysis. Details here.
• data: the data samples utilised in our experimental setup. Details here.

Requirements

>= python 3.9

# install dependencies
pip install -r requirements.txt
# install transformers from the repository
pip install git+https://github.com/huggingface/transformers

Models

Details here.

Model	Release Date	Sizes	Access	Max Length
GPT-2	11.2019	1.5B	Open	1,024
GPT-3	06.2020	175B	Limited	2,049
GPT-3.5	03.2023	Unknown	Limited	4,096
GPT-4	03.2023	Unknown	Limited	8,192
BLOOM	07.2022	560M, 3B, 7.1B	Open	2,048
BLOOMZ	11.2022	560M, 3B, 7.1B	Open	2,048
OPT	05.2022	350M, 6.7B	Open	2,048
LLaMA	02.2023	7B	Open	2,048
LLaMA-2	07.2023	7B	Open	4,096

Datasets

Details here.

Dataset	Document Type	Origin	Time Period	Language	# Lines	# Sentences	# Regions
icdar-2017	newspapers, monographies	OCR	17C-20C	en, fr	0	461	28
icdar-2019	OCR		not specified	bg, cz, en, fr, de, pl, sl	0	404	41
overproof	newspaper	OCR	19-20C	en	2,278	399	41
impresso-nzz	newspaper	OCR	18-20C	de	1,256	577	203
ajmc-mixed	class. commentaries	OCR	19C	grc, de, en, fr	535	379	33
ajmc-primary	class. commentaries	OCR	19C	grc, de, en, fr	40	27	9
htrec	papyri and manuscripts	HTR	10C-16C	grc	180	8	8
ina	radio programs	ASR	20C	fr	201	290	6

Experiments

Details here.

Publication

Emanuela Boros, Maud Ehrmann, Matteo Romanello, Sven Najem-Meyer, and Frédéric Kaplan. 2024. Post-Correction of Historical Text Transcripts with Large Language Models: An Exploratory Study. In Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024), March 2024. Association for Computational Linguistics, St. Julians, Malta, 133–159. https://doi.org/10.18653/v1/2024.latechclfl-1.14

@inproceedings{boros_postcorrection_2024,
  title = {Post-{{Correction}} of {{Historical Text Transcripts}} with {{Large Language Models}}: {{An Exploratory Study}}},
  booktitle = {Proceedings of the 8th {{Joint SIGHUM Workshop}} on {{Computational Linguistics}} for {{Cultural Heritage}}, {{Social Sciences}}, {{Humanities}} and {{Literature}} ({{LaTeCH-CLfL}} 2024)},
  author = {Boros, Emanuela and Ehrmann, Maud and Romanello, Matteo and {Najem-Meyer}, Sven and Kaplan, Fr{\'e}d{\'e}ric},
  editor = {Bizzoni, Yuri and {Degaetano-Ortlieb}, Stefania and Kazantseva, Anna and Szpakowicz, Stan},
  year = 2024,
  month = mar,
  pages = {133--159},
  publisher = {Association for Computational Linguistics},
  address = {St. Julians, Malta},
  url = {https://aclanthology.org/2024.latechclfl-1.14/},
  urldate = {2026-07-09}
}

Follow-up work: ICDAR 2026 HIPE-OCRepair Competition

• HIPE-OCRepair 2026 website
• HIPE-eval shared task series website
• HIPE-OCRepair data
• HIPE-OCRepair evaluation toolkit

About

Impresso project

Impresso - Media Monitoring of the Past is an interdisciplinary research project that aims to develop and consolidate tools for processing and exploring large collections of media archives across modalities, time, languages and national borders. The first project (2017-2021) was funded by the Swiss National Science Foundation under grant No. CRSII5_173719 and the second project (2023-2027) by the SNSF under grant No. CRSII5_213585 and the Luxembourg National Research Fund under grant No. 17498891.

Copyright

Copyright (C) 2023 The Impresso team.

License

This program is provided as open source under the GNU Affero General Public License v3 or later.

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