UB Mannheim · PyLaia · Published September 28, 2023

GermanNewspapers-M1

Text Recognition

Description

Model for historical newspapers printed mainly in German language and Fraktur (Black Letter). Period: early 18th - mid 20th century Trained on 3 GT sets: https://github.com/UB-Mannheim/reichsanzeiger-gt https://github.com/UB-Mannheim/AustrianNewspapers https://github.com/UB-Mannheim/NZZ-black-letter-ground-truth

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Very low error rate0.7% CER

Character Error Rate (CER) measures the percentage of characters incorrectly recognised. Lower is better. This model scored 0.7% 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.

Words1,013,820
Lines197,879
Training Pages387
Model ID55331
Languages
GermanLatin