## 1 Introduction  
A large number of manuscripts have been collected over the years and continue to be written today. They have many applications and require automatic processing, Handwritten texts are still widely used in many fields, such as healthcare, library work, personnel management, archives, and other fields. The need to make them available in modern information exchange and search systems is growing. Automation greatly reduces the labor involved in processing correspondence and surveys, as well as transcribing historical manuscripts. The current OCR seems to be advanced enough to handle printed text [1; 2; 3], but the handwriting recognition system (HTR) still needs improvement.  
The differences between handwritten Cyrillic Kazakh and handwritten Latin Kazakh are less pronounced than the differences between the printed versions. However, there are still some differences in the way the characters are written. Handwritten Cyrillic Kazakh tends to be more angular and blocky, while handwritten Latin Kazakh is more cursive and rounded. In recent years, there has been a movement to switch from Cyrillic Kazakh to the Latin script. The Kazakh government has set a goal of fully transitioning to the Latin script by 2025, citing the Latin script's familiarity and ease of use as reasons for the change [4; 5].  
While there is a movement to transition to the Latin script, Cyrillic Kazakh will continue to be used for some time and is an important part of the Kazakh cultural heritage.  
Handwritten text has several characteristics related to variability both in class and outside of class: different instances of the same word written by different people may look different, while the same character written by the same author may look different. It varies depending on the context in which it is written. We attribute this gap to the lack of fully annotated manuscripts and the difficulty in obtaining them. In this article, we attempt to fill this gap by creating a general-sized visual text, reducing the need for annotations, and enriching the diversity of training data in both style and vocabulary.  
The Generative Adversarial Network (GAN) is an effective way to solve this problem. GANs offer the right way to learn deep concepts without extensive use of labeled learning data. This approach has caught the attention of many computer vision researchers because it allows them to generate large amounts of data without accurately changing the probability density function (PDF). In GAN, the generative model is evaluated using a competitive process in which generating and discriminating networks are trained simultaneously. The generator learns to extract reliable data, and the discriminator learns to distinguish fake data generated by the generator from real data patterns. Considering the fast growth of GANs over the past few years and their use in various fields, these networks need to be carefully studied.  
This article presents the main concepts of GAN, compares several models of deep SOTA generation, and explains the evaluation indicators used in the literature and the problems associated with GAN. Moreover, the most notable GAN architectures are classified and discussed.