Newseye-project · PyLaia · Published October 22, 2021

NLF_Newseye_GT_FI_M2+

Text Recognition

Description

The model works well with Finnish script from late 18th century to mid of 20th century. For normal running text in newspapers from that time error rates much below 1% were measured. The model was created in the NewsEye project and is based on training data coming from the National Library Finland (NLF). Note: the model is trained on Finnish language documents and will therefore be less performant on other languages.

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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.

Words746,500
Lines153,333
Training Pages526
Model ID37748
Languages
Finnish