  This article discusses the problem of handwriting recognition in Kazakh and
Russian languages. This area is poorly studied since in the literature there
are almost no works in this direction. We have tried to describe various
approaches and achievements of recent years in the development of handwritten
recognition models in relation to Cyrillic graphics. The first model uses deep
convolutional neural networks (CNNs) for feature extraction and a fully
connected multilayer perceptron neural network (MLP) for word classification.
The second model, called SimpleHTR, uses CNN and recurrent neural network (RNN)
layers to extract information from images. We also proposed the Bluechet and
Puchserver models to compare the results. Due to the lack of available open
datasets in Russian and Kazakh languages, we carried out work to collect data
that included handwritten names of countries and cities from 42 different
Cyrillic words, written more than 500 times in different handwriting. We also
used a handwritten database of Kazakh and Russian languages (HKR). This is a
new database of Cyrillic words (not only countries and cities) for the Russian
and Kazakh languages, created by the authors of this work.
