j.helmchen · PyLaia · Published November 5, 2022
Generic Model 15th-16th century German (prototype)
Text Recognition
Description
(Version für TUC 2022)
The model is based on a selection of late medieval German manuscripts from the fifteenth and sixteenth centuries.
With a geographical focus on Austria and the Hanseatic Baltic region, both Middle Low and Early New High German texts are represented in the training material. Among the fonts used, Gothic cursive, Bastarda, and early Kurrent are most common.
The training data set is composed of approximately 77.000 words. The ground truth was compiled from several projects currently in progress at various institutions. The aim is to significantly expand and diversify this in the future to train a generic model. The current model therefore has a prototype status, with a CER of 5.60% on a validation set.
Contact: j.helmchen@fu-berlin.de
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Low error rate5.6% CER
Character Error Rate (CER) measures the percentage of characters incorrectly recognised. Lower is better. This model scored 5.6% on its validation set. As a rule of thumb, a CER below 10% is considered good for most handwritten material.
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.
Words79,204
Lines8,240
Training Pages262
Model ID45902