yngvil.beyer · PyLaia · Published April 7, 2022

NorFraktur_1600_PyLaia

Text Recognition

Description

Model trained on 77 Danish-Norwegian and 23 German small blackletter prints (broadside ballads - "skillingsviser") compiled in "Peder Rafns visebog", København 1583-1634 In the training data the different printing practices of the period are reflected. The letters i, j, u and v are transcribed diplomatic; i.e. as they appear. Ligatures β, ꜩ and œ are transcribed s, tz and oe. n/m with a macron is trancribed nn/mm, and the rotunda ꝛ is transcribed r.

Try this model

Use this modelOpen in Transkribus
Very low error rate2% CER

Character Error Rate (CER) measures the percentage of characters incorrectly recognised. Lower is better. This model scored 2% 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.

Words106,211
Lines21,253
Training Pages856
Model ID40982
Languages
DanishGermanNorwegian