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Handwritten Text Recognition technology and MS Turin, BNU, L.II.14 (T). The “Rescapé’’ case study

Patricia O’connor
p. 367-376

Abstract

The objective of this contribution is to report on the Rescapé project’s experiments in applying HTR technology to the fire-damaged T manuscript. This contribution offers an overview of the creation of the Rescapé dataset and details its compliance with the SegmOnto controlled vocabulary. This case study concentrates on the challenges which fire-damaged manuscripts pose to HTR technology and highlights the effectiveness of resources like YALTAi in optimising the segmentation process. It concludes with a discussion of how the resulting dataset forms a fundamental foundation for future HTR models aimed at analysing damaged medieval manuscripts.

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Estratto del testo

Questo documento sarà pubblicato online con testo integrale in agosto 2026.

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1. Introduction: “Rescapé”
2. The T“Rescapé“ ” dataset: contents and creation
3. Preliminary findings and discussion
4. Segmentation challenges and solution
5. Transcription
6. Future work
7. Conclusion

Anteprima del testo

1. Introduction: “Rescapé”

The Rescapé project is a collaboration at PSL University between the Centre Jean-Mabillon at the École nationale des chartes and EquipEx Biblissima+ at Campus Condorcet. Funded by Biblissima+, Rescapé sought to apply HTR to the fire-damaged T manuscript. A machine-learning approach for segmenting and transcribing images of handwritten documents, the purpose of Rescapé is to avail of the affordances of HTR technology to recognise both the layout structures and the textual content of this fire-damaged medieval manuscript. The segmentation process is the first stage in the HTR approach and focuses on analysing the layout of a manuscript, in other words, recognising the different regions on a manuscript page. Once the segmentation stage is completed satisfactorily, the second and final stage is the transcription process which consists of identifying and transcribing the textual content of the manuscript. While the manuscript’s sizeable corpus justified the use ...

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Notizia bibliografica

Patricia O’connor, «Handwritten Text Recognition technology and MS Turin, BNU, L.II.14 (T). The “Rescapé’’ case study»Studi Francesi, 206 (LXIX | II) | 2025, 367-376.

Notizia bibliografica digitale

Patricia O’connor, «Handwritten Text Recognition technology and MS Turin, BNU, L.II.14 (T). The “Rescapé’’ case study»Studi Francesi [Online], 206 (LXIX | II) | 2025, online dal 01 agosto 2026, consultato il 15 marzo 2026. URL: http://journals.openedition.org/studifrancesi/65446; DOI: https://doi.org/10.4000/15gi9

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Autore

Patricia O’connor

Maynooth University

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CC-BY-NC-ND-4.0

The text only may be used under licence CC BY-NC-ND 4.0. All other elements (illustrations, imported files) may be subject to specific use terms.

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