Álvaro Cuéllar · PyLaia · Published March 24, 2024

LopeDetector

Text Recognition

Description

This model is capable of detecting Lope de Vega's handwriting in a large textual corpus. If it finds Lope's handwriting, it marks it with an X; if it finds other scribes' handwriting, it marks it with an O. For more information, see: Cuéllar, Á., & Boadas, S. (2025). Artificial Intelligence for Calligraphic Writer Identification: The Case of Lope de Vega’s Autographs. Hipogrifo. Revista de literatura y cultura del Siglo de Oro, 13(1), 517–532. https://doi.org/10.13035/H.2025.13.01.36.

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Low error rate7.2% CER

Character Error Rate (CER) measures the percentage of characters incorrectly recognised. Lower is better. This model scored 7.2% 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.

Words2,897,977
Lines926,425
Training Pages15,632
Model ID61281
Languages
Castilian
Centuries
16th c.17th c.