Transkribus · PyLaia · Published October 28, 2021

HIMANIS Chancery M1+

Text Recognition

Description

As part of the HIMANIS project (lead by D. Stutzmann, C. Kermorvant & E. Vidal), the text edition provided by P. Guérin and encoded in TEI by the Ecole nationale des Chartes (http://corpus.enc.sorbonne.fr/actesroyauxdupoitou/) and the one by J. Viard were aligned at line level and used to train this comprehensive model for French and Latin Chancery documents. More information on the project can be found at: http://himanis.huma-num.fr/himanis/

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Low error rate6.5% CER

Character Error Rate (CER) measures the percentage of characters incorrectly recognised. Lower is better. This model scored 6.5% 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.

Words665,988
Lines39,956
Training Pages1,364
Model ID37839