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        <title>Pre-Editorial Normalization for Automatically Transcribed Medieval Manuscripts in Old French and Latin</title>
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    <name type="personal">
        <namePart type="given">Ariane</namePart>
        <namePart type="family">Pinche</namePart>
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            <title>Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026</title>
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            <namePart type="given">Rachele</namePart>
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    <abstract>Recent advances in Automatic Text Recognition (ATR) have improved access to historical archives, yet a methodological divide persists between palaeographic transcriptions and normalized digital editions. While ATR models trained on more palaeographically-oriented datasets such as CATMuS have shown greater generalizability, their raw outputs remain poorly compatible with most readers and downstream NLP tools, thus creating a usability gap. On the other hand, ATR models trained to produce normalized outputs have been shown to struggle to adapt to new domains and tend to over-normalize and hallucinate. We introduce the task of Pre-Editorial Normalization (PEN), which consists in normalizing graphemic ATR output according to editorial conventions, which has the advantage of keeping an intermediate step with palaeographic fidelity while providing a normalized version for practical usability. We present a new dataset derived from the CoMMA corpus and aligned with digitized Old French and Latin editions using passim. We also produce a manually corrected gold-standard evaluation set. We benchmark this resource using ByT5-based sequence-to-sequence models on normalization and pre-annotation tasks. Our contributions include the formal definition of PEN, a 4.66M-sample silver training corpus, a 1.8k-sample gold evaluation set, and a normalization model achieving a 6.7% CER, substantially outperforming previous models for this task</abstract>
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%0 Conference Proceedings
%T Pre-Editorial Normalization for Automatically Transcribed Medieval Manuscripts in Old French and Latin
%A Clérice, Thibault
%A Bawden, Rachel
%A Glaise, Anthony
%A Pinche, Ariane
%A Smith, David
%Y Sprugnoli, Rachele
%Y Passarotti, Marco
%S Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F clerice-etal-2026-pre
%X Recent advances in Automatic Text Recognition (ATR) have improved access to historical archives, yet a methodological divide persists between palaeographic transcriptions and normalized digital editions. While ATR models trained on more palaeographically-oriented datasets such as CATMuS have shown greater generalizability, their raw outputs remain poorly compatible with most readers and downstream NLP tools, thus creating a usability gap. On the other hand, ATR models trained to produce normalized outputs have been shown to struggle to adapt to new domains and tend to over-normalize and hallucinate. We introduce the task of Pre-Editorial Normalization (PEN), which consists in normalizing graphemic ATR output according to editorial conventions, which has the advantage of keeping an intermediate step with palaeographic fidelity while providing a normalized version for practical usability. We present a new dataset derived from the CoMMA corpus and aligned with digitized Old French and Latin editions using passim. We also produce a manually corrected gold-standard evaluation set. We benchmark this resource using ByT5-based sequence-to-sequence models on normalization and pre-annotation tasks. Our contributions include the formal definition of PEN, a 4.66M-sample silver training corpus, a 1.8k-sample gold evaluation set, and a normalization model achieving a 6.7% CER, substantially outperforming previous models for this task
%R 10.63317/3oc8ht2umo8o
%U https://aclanthology.org/2026.lt4hala-1.48/
%U https://doi.org/10.63317/3oc8ht2umo8o
%P 453-468

Markdown (Informal)

[Pre-Editorial Normalization for Automatically Transcribed Medieval Manuscripts in Old French and Latin](https://aclanthology.org/2026.lt4hala-1.48/) (Clérice et al., LT4HALA 2026)

• Pre-Editorial Normalization for Automatically Transcribed Medieval Manuscripts in Old French and Latin (Clérice et al., LT4HALA 2026)

ACL

• Thibault Clérice, Rachel Bawden, Anthony Glaise, Ariane Pinche, and David Smith. 2026. Pre-Editorial Normalization for Automatically Transcribed Medieval Manuscripts in Old French and Latin. In Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026, pages 453–468, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).