pavlas.petr · PyLaia · Published September 20, 2024

TOME 3.0

Text Recognition

Description

The model is trained on early modern Latin print and is based on Noscemus GM 6.0. Use this model for printed text if it is predominantly in Latin. It is fairly proficient for other Latin-based scripts, but will not perform as well for mixed scripts. Its main training base was cursive Latin, but we have also used italic. The model was trained chiefly on history of alchemy printed text. It has been trained to detect some astrological and alchemical symbols (such as those of Sun/gold and Mercury) and convert them to Latin equivalents, but results may require refinement.

Try this model

Use this modelOpen in Transkribus
Very low error rate0.58% CER

Character Error Rate (CER) measures the percentage of characters incorrectly recognised. Lower is better. This model scored 0.58% 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.

Words365,313
Lines59,459
Training Pages1,983
Model ID178745
Languages
Latin
Centuries
1st c.2nd c.3rd c.4th c.5th c.6th c.7th c.8th c.9th c.10th c.11th c.12th c.13th c.14th c.15th c.16th c.17th c.18th c.19th c.20th c.21st c.