L'équipe de l'APAD / Jean-Luc Lauzon · PyLaia · Published December 19, 2024

Les notaires montréalais: Closse, Frérot, Gastineau, Ménard, Mouchy, St-Père Nouvelle-Fr...

Text Recognition

Description

Modèle développé avec le projet: "Donner le goût de l'archive à l'ère numérique" (https://donner-le-gout-de-larchive.weebly.com), de l’Université de Montréal, Bibliothèque et archives nationales du Québec, Société de recherches Archiv-Histo et l’Atelier Permanent d'analyse documentaire Volet transcription. Modèle fait à partir des actes du greffe des notaires Closse, Frérot, Gastineau, Ménard, Mouchy, Oudain, Rémy, et St-Père. This model was developed as part of the project ‘Donner le goût de l'archive à l'ère numérique’ (https://donner-le-gout-de-larchive.weebly.com) at the Université de Montréal, Bibliothèque et archives nationales du Québec, Société de recherches Archiv-Histo and Atelier Permanent d'analyse documentaire- Volet transcription. Based on deeds by notary Closse, Frérot, Gastineau, Ménard, Mouchy, Oudain, Rémy, and St-Père

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Moderate error rate14.06% CER

Character Error Rate (CER) measures the percentage of characters incorrectly recognised. Lower is better. This model scored 14.06% on its validation set. As a rule of thumb, a CER below 10% is considered good for most handwritten material. This is a larger model trained on diverse material, which generally makes it more robust across different handwriting styles. That said, larger training sets also make it harder to push the CER down further.

Measured on the model's own validation data. Results on your documents may differ depending on handwriting style, document condition, language, and how closely your material resembles the training data.

Words183,125
Lines28,061
Training Pages963
Model ID248369
Languages
French
Centuries
17th c.