# Handling Heavily Abbreviated Manuscripts: HTR engines vs text normalisation approaches

arXiv: https://arxiv.org/abs/2107.03450 · HTML: https://mlsanity.com/paper/2107.03450/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 7 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [A Large-Scale Comparison of Historical Text Normalization Systems](https://mlsanity.com/paper/1904.02036/) (2019)
- [Improving Lemmatization of Non-Standard Languages with Joint Learning](https://mlsanity.com/paper/1903.06939/) (2019)
- [Important New Developments in Arabographic Optical Character Recognition (OCR)](https://mlsanity.com/paper/1703.09550/) (2017)
- [Stylometry for Noisy Medieval Data: Evaluating Paul Meyer's Hagiographic Hypothesis](https://mlsanity.com/paper/2012.03845/) (2020)

## Built on by (most cited first, 2 of 2)

- [New Results for the Text Recognition of Arabic Maghrib=i Manuscripts -- Managing an Under-resourced Script](https://mlsanity.com/paper/2211.16147/) (2022)
- [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)

## Abstract (arXiv)

Although abbreviations are fairly common in handwritten sources, particularly in medieval and modern Western manuscripts, previous research dealing with computational approaches to their expansion is scarce. Yet abbreviations present particular challenges to computational approaches such as handwritten text recognition and natural language processing tasks. Often, pre-processing ultimately aims to lead from a digitised image of the source to a normalised text, which includes expansion of the abbreviations. We explore different setups to obtain such a normalised text, either directly, by training HTR engines on normalised (i.e., expanded, disabbreviated) text, or by decomposing the process into discrete steps, each making use of specialist models for recognition, word segmentation and normalisation. The case studies considered here are drawn from the medieval Latin tradition.