National Library of Norway · PyLaia · Published September 20, 2024

SamiskOCR_alt

Text Recognition

Description

This is a multilingual Sámi model trained on printed text in North Sámi, South Sámi, Lule Sámi and Inari Sámi. The model is trained on pages from books and newspapers from The National Library of Norway’s collection. The training material consists of Sámi texts written in the contemporary written standards of the four Sámi languages. In total, the model is trained on 485 407 words, and it achieves a CER of 1.18% on the validation set.

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Very low error rate1.18% CER

Character Error Rate (CER) measures the percentage of characters incorrectly recognised. Lower is better. This model scored 1.18% 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.

Words485,407
Lines93,603
Training Pages2,513
Model ID179305
Languages
NorwegianSami Languages