Authors: Simon Gabay ^1,2,3; Thibault Clérice ⁴; Christian Reul ^5,6,7,8

• 1 Université de Genève = University of Geneva
• 2 Université de Neuchâtel
• 3 Université de Neuchâtel = University of Neuchatel
• 4 Centre Jean Mabillon
• 5 University of Würzburg = Universität Würzburg
• 6 Julius-Maximilians-Universität Würzburg
• 7 Julius-Maximilians-Universität Würzburg = University of Würzburg [JMU]
• 8 Julius-Maximilians-Universität Würzburg = University of Würzburg [Würsburg, Germany] [JMU]

Machine learning begins with machine teaching: in the following paper, we present the data that we have prepared to kick-start the training of reliable OCR models for 17th century prints written in French. The construction of a representative corpus is a major challenge: we need to gather documents from different decades and of different genres to cover as many sizes, weights and styles as possible. Historical prints containing glyphs and typefaces that have now disappeared, transcription is a complex act, for which we present guidelines. Finally, we provide preliminary results based on these training data and experiments to improve them.