igor.sosa · PyLaia · Published November 5, 2025

Wecker Antodotarium v1.1

Text Recognition

Description

Model based on GM Noscemos 6.0 and trained on 50 pages of the work Antodotarium generale et specialis by Johannes Wecker. It follows the settings of Noscemus regarding abbreviations. But it includes pages with (1) symbols used for weights in receipts (ʒ, ℥, etc.) and (2) text in cursive font. Unclear and gaps were marked for being excluded in the training.

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Very low error rate1.78% CER

Character Error Rate (CER) measures the percentage of characters incorrectly recognised. Lower is better. This model scored 1.78% on its validation set. As a rule of thumb, a CER below 10% is considered good for most handwritten material. This is a smaller, specialised model. It may achieve a very low CER on material similar to its training data, but could be less robust on unfamiliar handwriting or layouts.

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.

Words32,715
Lines5,538
Training Pages45
Model ID427241
Languages
Latin
Centuries
16th c.17th c.