# A Large-Scale Comparison of Historical Text Normalization Systems

arXiv: https://arxiv.org/abs/1904.02036 · HTML: https://mlsanity.com/paper/1904.02036/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2019.

## Built on by (most cited first, 25 of 25)

- [ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper Pages](https://mlsanity.com/paper/2403.17859/) (2024)
- [Cross-lingual Cross-temporal Summarization: Dataset, Models, Evaluation](https://mlsanity.com/paper/2306.12916/) (2023)
- [Summarising Historical Text in Modern Languages](https://mlsanity.com/paper/2101.10759/) (2021)
- [When Word Embeddings Become Endangered](https://mlsanity.com/paper/2103.13275/) (2021)
- [Lemmatization of Historical Old Literary Finnish Texts in Modern Orthography](https://mlsanity.com/paper/2107.03266/) (2021)
- [Multilingual Event Extraction from Historical Newspaper Adverts](https://mlsanity.com/paper/2305.10928/) (2023)
- [PHD: Pixel-Based Language Modeling of Historical Documents](https://mlsanity.com/paper/2310.18343/) (2023)
- [Text normalization for low-resource languages: the case of Ligurian](https://mlsanity.com/paper/2206.07861/) (2022)
- [Measuring Intersectional Biases in Historical Documents](https://mlsanity.com/paper/2305.12376/) (2023)
- [Make Every Letter Count: Building Dialect Variation Dictionaries from Monolingual Corpora](https://mlsanity.com/paper/2509.17855/) (2025)
- [Handling Heavily Abbreviated Manuscripts: HTR engines vs text normalisation approaches](https://mlsanity.com/paper/2107.03450/) (2021)
- [An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3](https://mlsanity.com/paper/2506.00312/) (2025)
- [Digitizing Historical Balance Sheet Data: A Practitioner's Guide](https://mlsanity.com/paper/2204.00052/) (2022)
- [Is text normalization relevant for classifying medieval charters?](https://mlsanity.com/paper/2408.16446/) (2024)
- [Comprehensive Evaluation on Lexical Normalization: Boundary-Aware Approaches for Unsegmented Languages](https://mlsanity.com/paper/2505.22273/) (2025)
- [Unveiling Factors for Enhanced POS Tagging: A Study of Low-Resource Medieval Romance Languages](https://mlsanity.com/paper/2506.17715/) (2025)
- [Dealing with Abbreviations in the Slovenian Biographical Lexicon](https://mlsanity.com/paper/2211.02429/) (2022)
- [Two Approaches to Diachronic Normalization of Polish Texts](https://mlsanity.com/paper/2402.01300/) (2024)
- [Historical German Text Normalization Using Type- and Token-Based Language Modeling](https://mlsanity.com/paper/2409.02841/) (2024)
- [Modelling Intertextuality with N-gram Embeddings](https://mlsanity.com/paper/2509.06637/) (2025)
- [The InviTE Corpus: Annotating Invectives in Tudor English Texts for Computational Modeling](https://mlsanity.com/paper/2509.22345/) (2025)
- [Neural Induction of Finite-State Transducers](https://mlsanity.com/paper/2601.10918/) (2026)
- [Letter Lemmatization: One-to-one and Banded RNNs for Reversing Character-Set Simplification and Abbreviation in Medieval Text](https://mlsanity.com/paper/2607.09291/) (2026)
- [Automatic Thematic Indexing of Large Literary Corpora: A Machine Learning Approach to Voltaire's Complete Works](https://mlsanity.com/paper/2607.09316/) (2026)
- [Correction as Annotation: Bootstrapping a Dependency Parser for Documentary Medieval Latin](https://mlsanity.com/paper/2609.06266/) (2026)

## Abstract (arXiv)

There is no consensus on the state-of-the-art approach to historical text normalization. Many techniques have been proposed, including rule-based methods, distance metrics, character-based statistical machine translation, and neural encoder--decoder models, but studies have used different datasets, different evaluation methods, and have come to different conclusions. This paper presents the largest study of historical text normalization done so far. We critically survey the existing literature and report experiments on eight languages, comparing systems spanning all categories of proposed normalization techniques, analysing the effect of training data quantity, and using different evaluation methods. The datasets and scripts are made publicly available.