## 3 Results
### Parameters of Proposed Models
The noise vector is responsible for the style of writing: handwriting/thickness/italics, and so on. The generated image is fed into the discriminate input, which helps to improve the overall quality of the image, and to a recognizer (OCR), which makes the text readable. The authors of the article store only convolutional layers in OCR models for the reason that recurrent ones can learn the implicit language model of the data set and predict the correct description of words, while in the picture it is written erroneously (implicit language model in LSTM for OCR). This can get in the way when OCR is used as part of the GAN architecture, because here OCR should read exactly the text that the generator wrote without thinking it through. In the table, the results of each model with different losses. For all models, we use the Adam optimizer with \(\beta\)1 = 0 and \(\beta\)2 = 0.8 for training. By default, the learning rate for the discriminator is 0.0004, and the learning rate for the generator is 0.0001. As shown in Table 1, the model ScrabbleGAN achieves the best IS, FID, and SSIM scores among all generative models. The model significantly improves the best published Inception score from 35.24 to 52.21. We found that different loss functions help improve classifier sensitivity to imperfect GAN outputs. Accordingly, as generative modeling technology advances in the future to generate better reconstructions, similar GAN upscaling and merging strategies may lead to even greater improvements. From the results of images generated on the TextStyleBrush, GanWriting, and ScrabbleGAN models, it appears that TextStyleBrush generates an image with little handwriting variation, which is very important for the OCR model. GanWriting has difficulty with similar letters such as \"\({}_{\text{M}}\)\", \"\({}_{\text{M}}\)\", \"\({}_{\text{M}}\)\". ScrabbleGAN is very good in terms of handwriting variation, but there are certain difficulties with capital letters; this is due to the fact that their ratio was very small in the dataset. In the next experiment, we will use only the ScrabbleGan model for generating images due to the model's good results (Figure 2). _Experiment 4._ The purpose of the experiment is to evaluate how our data generation approach will help improve the accuracy of handwriting recognition on real data depending on the quantity and quality of images. In this experiment, we present a transformer-based architecture for handwriting text recognition with a lightweight convolutional encoder (Figure 3). Transformed-based models use data augmentation techniques to transform the original dataset into a larger set of training examples [34]. This technique helps improve the model's accuracy by reducing overfitting and increasing the diversity of the training examples. The size of the dataset is a critical factor in determining the performance of transformed-based models. To evaluate the model, we use character error rate (CER) and word error rate (WER) metrics [35]. To assess how much generated data helps for metrics, we conducted several experiments adding different amounts of data to the real dataset. First, we trained our model only on real data with a total of 45,211 words. After that, we generated an image by adding 30%, 50%, and 100% for the number of each word in the dataset; in total, we obtained 14 k, 22 k, and 44 k generated images of words, respectively. On different amounts of data, our model was trained, and the results are shown in the table below. From Table 2, we can see that the average WER (%) and CER (%) of the trained models tested on different combinations of datasets. The WER and CER results showed 25.65% and 11.15%, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **IS** & **FID** & **SSIM** \\ \hline TextStyleBrush & 35.24 & 32.11 & 0.2 \\ \hline GanWriting & 49.33 & 29.23 & 0.38 \\ \hline ScrabbleGAN & 51.21 & 20.14 & 0.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparing the influence of different loss functions on GAN models’ results.