lali_kakhidze · CITlabPlus · Published April 27, 2022

German_Kurrent_XIX_NAG_GEO_ISU

Text Recognition

Description

This model trained with an archival documents about German Colonists in Georgia in frame of a PhD research at Ilia State University (ISU), Tbilisi, Georgia. These documents are preserved in National Archives of Georgia (NAG) with open access. Research data are handwritten German language (in Kurrentschrift) sources with some Russian texts about Katharinenfeld. Time period XIX (XX) century. This model is suitable for archival documents in German Kurrentschrift (used in Kaukasus). Model trained by Lali Kakhidze.

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Low error rate8.59% CER

Character Error Rate (CER) measures the percentage of characters incorrectly recognised. Lower is better. This model scored 8.59% 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.

Words537,060
Lines110,099
Training Pages3,146
Model ID41452
Languages
GermanRussian