We introduce the largest (among publicly available) dataset for Cyrillic Handwritten Text Recognition and the first dataset for Cyrillic Text in the Wild Recognition, as well as suggest a method for recognizing Cyrillic Handwritten Text and Text in the Wild. Based on this approach, we develop a system that can reduce the document processing time for one of the largest mathematical competitions in Ukraine by 12 days and the amount of used paper by 0.5 ton. Text is one of the main ways to transfer the information and it can take many forms. It can be handwritten or printed, in the form of business documents, notes, bills, historical documents, advertisements, logos etc. Therefore, the method for its recognition should be flexible enough to work with different text styles and under the different external conditions. Although for the English language the task of text recognition is well studied [1] , [2] , for Cyrillic languages such studies are almost missing, the main reason being the lack of extensive publicly available datasets. To the best of our knowledge, the only public Cyrillic dataset consists only of individual letters [3] , while others [1] , [4] , [5] , are unavailable. In our research, we will focus on developing a single model for Handwritten Text Recognition and Text Recognition in the Wild, as the extreme case of printed text.
We introduce several datasets for Cyrillic OCR and a method for its recognition
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