MultiHTR project (Achim Rabus & Aleksej Tikhonov) · PyLaia · Published May 3, 2023

Ukrainian generic handwriting 1

Text Recognition

Description

This first version of a generic model for handwritten Ukrainian (predominantly from the second part of the 19th to the late 20th century) was curated and trained by Aleksej Tikhonov, a researcher of the MultiHTR project (Freiburg/Germany, PI: Achim Rabus, www.multihtr.uni-freiburg.de). Portions of the GT data have kindly been provided by Kateryna Lobuzina of The Vernadskyi National Library of Ukraine (VNLU) (http://nbuv.gov.ua/node/554), containing manuscripts by Taras Shevchenko, as well as Klyment Kvitka’s notices and comments on Ukrainian folklore collected by Lesya Ukrainka. Another part of the GT has kindly been provided by the corpus created by Maria Shvedova, Ruprecht von Waldenfels, Serhij Yaryhin, Andriy Rysin, Vasyl Starko, Tymofij Nikolajenko, et al. (2017-2023): GRAC: General Regionally Annotated Corpus of Ukrainian, Electronic resource: Kyiv, Lviv, Jena (uacorpus.org), which contains a collection of letters prepared by students of Lviv Polytechnic University in 2018-2020 (http://uacorpus.org/Kyiv/en/gracinfo/rozrobniki). We thank Mykhailo Kostiv of The National Museum of the Holodomor-Genocide in Kyiv (https://holodomormuseum.org.ua/en), who provided the training process with a manuscript by Lavrin Nechyporenko from the 1960s. We sincerely thank our partners from Ukraine for the cooperation with MultiHTR on the first generic model of handwritten Ukrainian, despite the ongoing war in their country.

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Very low error rate4.2% CER

Character Error Rate (CER) measures the percentage of characters incorrectly recognised. Lower is better. This model scored 4.2% 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.

Words100,079
Lines17,591
Training Pages541
Model ID51906
Languages
Ukrainian