Auteurs : Ariane Pinche ^1,2; Peter Stokes ^3,4

• 1 Centre National de la Recherche Scientifique [CNRS]
• 2 Histoire, Archéologie et Littératures des mondes chrétiens et musulmans médiévaux [CIHAM]
• 3 Archéologie et Philologie d'Orient et d'Occident [EPHE]
• 4 École Pratique des Hautes Études [EPHE]

With this special issue of the Journal of Data Mining and Digital Humanities (JDMDH), we bringtogether in one single volume several experiments, projects and reflections related to automatic textrecognition applied to historical documents.More and more research projects1 now include automatic text acquisition in their data processing chain,and this is true not only for projects focussed on Digital or Computational Humanities but increasinglyalso for those that are simply using existing digital tools as the means to an end. The increasing useof this technology has led to an automation of tasks that affects the role of the researcher in the textualproduction process. This new data-intensive practice makes it urgent to collect and harmonise the corporanecessary for the constitution of training sets, but also to make them available for exploitation. Thisspecial issue is therefore an opportunity to present articles combining philological and technical questionsto make a scientific assessment of the use of automatic text recognition for ancient documents, itsresults, its contributions and the new practices induced by its use in the process of editing and exploringtexts. We hope that practical aspects will be questioned on this occasion, while raising methodologicalchallenges and its impact on research data.The special issue on Automatic Text Recognition (ATR) is therefore dedicated to providing a comprehensiveoverview of the use of ATR in the humanities field, particularly concerning historical documentsin the early 2020s. This issue presents a fusion of engineering and philological aspects, catering to bothbeginners and experienced users interested in launching projects with ATR. The collection encompassesa diverse array of approaches, covering topics such as data creation or collection for training genericmodels, reaching specific objectives, technical and HTR machine architecture, segmentation methods,and image processing.