Álvaro Cuéllar · PyLaia · Published November 13, 2022

Spanish Golden Age Prints (Spelling Modernization) 1.0

Text Recognition

Description

If you use this model, please cite: -Cuéllar, Álvaro. (2023). «La Inteligencia Artificial al rescate del Siglo de Oro. Transcripción y modernización automática de mil trescientos impresos y manuscritos teatrales», Hipogrifo. Revista de literatura y cultura del Siglo de Oro, vol. 11, núm. 1, pp. 101-115, https://doi.org/10.13035/H.2023.11.01.08. -Cuéllar, Álvaro. "Spanish Golden Age Prints (Spelling Modernization) 1.0". Transkribus. 2021 -Cuéllar, Álvaro and Germán Vega García-Luengos. ETSO: Estilometría aplicada al Teatro del Siglo de Oro. 2017-2023. https://etso.es/. Model trained by Álvaro Cuéllar for the project ETSO: Estilometría aplicada al Teatro del Siglo de Oro (http://etso.es). It is able to modernize the spelling to the current rules. It has been trained with theatrical prints, mostly 17th and 18th centuries. If you want more information on how to use this model or you want to collaborate somehow, please write to alvarocuellar1995@hotmail.com.

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Use this modelOpen in Transkribus
Very low error rate3.1% CER

Character Error Rate (CER) measures the percentage of characters incorrectly recognised. Lower is better. This model scored 3.1% 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.

Words2,757,908
Lines904,457
Training Pages9,323
Model ID46161
Languages
Castilian
Centuries
17th c.18th c.