Letter Lemmatization: One-to-one and Banded RNNs for Reversing Character-Set Simplification and Abbreviation in Medieval Text
This paper introduces letter lemmatization using one-to-one RNNs with self-supervision to reverse character-set simplifications, achieving half CER recovery from just 20 text lines. It also employs Banded RNNs for abbreviation expansion in medieval charters and presents a heuristic for semantic similarity between character sets, along with a Python library.
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[Submitted on 10 Jul 2026]
Title:Letter Lemmatization: One-to-one and Banded RNNs for Reversing Character-Set Simplification and Abbreviation in Medieval Text
View a PDF of the paper titled Letter Lemmatization: One-to-one and Banded RNNs for Reversing Character-Set Simplification and Abbreviation in Medieval Text, by Anguelos Nicolaou and 4 other authors
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Abstract:Medieval document transcribers have very different practices; on top of that, heterogeneous digitization policies have resulted in corpora where the character-set must be viewed as fluid. In this paper we address the problem of changing between character-sets in a flexible manner. We focus on one-to-one character mappings and train characterlevel one-to-one RNNs to undo them with self-supervision; recovering half the CER even with 20 text lines. We analyse the use of these one-to-one networks for HTR post-correction and we see that they obtain significant improvements while totally ignoring ins-dels. We then use the exact same networks with character-level alignment groundtruth compiled from parallel corpora in a training and inference mode we call Banded RNNs. We use such networks to successfully expand abbreviations in medieval charter transcriptions. Finally we introduce an elaborate heuristic which takes the characters of two arbitrary character-sets and defines a metric encapsulating what we consider to be semantic similarity of characters. We call the construction of such mappings letter lemmatization and present a rich Python library that efficiently performs all presented methods.
Comments: Accepted for publication (after peer review ) in the ICDAR 2026 workshop "VINALDO: 3rd International Workshop on Machine Vision and NLP for Document Analysis"
Subjects:
Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.09291 [cs.CL]
(or arXiv:2607.09291v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.09291
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Anguelos Nicolaou [view email] [v1] Fri, 10 Jul 2026 11:07:51 UTC (94 KB)
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