Recognition of Church Slavonic Texts Using Machine Learning Methods

This study explores an approach to recognizing texts of ancient printed books written in Church Slavonic, utilizing computer vision and neural network methods. We achieved a classification accuracy of 99.13% for Church Slavonic alphabet characters, including punctuation marks, and 98.58% for superscript signs. The study developed an application variant enabling the conversion of Church Slavonic text images into an editable format. We formed datasets comprising image samples of letters and superscript signs, featuring no fewer than 200 examples per letter and at least 150 images per sign.