## 4 Experiments
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Name** & **Steps** & **Epochs** & **Best\_Acc** & **Distribution** & **Vocab Size** \\ \hline
15M\_enhujp\_v2\_1 & 1.32 M & 100 & 85\% & Figure 7 & 2737 \\
30M\_enhujp\_v2\_4 & 0.7 M & 10 & 98.76\% & Figure 8 & 1441 \\
50M\_enhujp\_v2\_2 & 1.71 M & 10 & 94\% & Figure 9 & 3964 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Data distribution and results. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Name** & **Steps** & **Epochs** & **Best\_Acc** & **Dataset** & **Vocab Size** \\ \hline
3M\_dashboard\_eng\_V1 & 1.83 M & 90 & 91\% & 3 M & 803 \\
3M\_dashboard\_hun\_v1 & 0.96 M & 47 & 89\% & 3 M & 112 \\
3M\_dashboard\_enhip\_v2\_1 & 1.61 M & 100 & 90\% & 3 M & 4444 \\
10M\_enhip\_v2\_1 & 3.96 M & 100 & 85\% & 8.5 M & 4444 \\
15M\_enhip\_v2\_1 & 1.32 M & 100 & 85\% & 15 M & 2737 \\
30M\_enhip\_v2\_4 & 0.7 M & 10 & 98.76\% & 30 M & 1441 \\
30M\_enhip\_v2\_1 & 0.15 M & 10 & 95.87\% & 30 M & 1441 \\
50M\_enhip\_v2\_2 & 1.71 M & 10 & 94\% & 50 M & 3964 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results of feasibility testing. \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Project** & **Name** & **Dictionary** & **Epochs** & **Text** & **Training** & **Evaluation** & **Image** & **Learning** & **Train** & **Training** & **Evaluation** \\
**Type** & & & & **Length** & **Batch Size** & **Batch Size** & **Shape** & **Rate** & **Best Acc** & **Size** & **Size** \\ \hline Single language & 158\_hu\_v2\_2 & 606k\_hum\_vocab\_txt & 2000 & 100 & 128 & 128 & \(\{3,32,512\}\) & 0.0050 & 0.949 & 77 k & 15 k \\ Single language & 155\_hu\_v2\_1 & 606k\_hum\  
Figure 8: Text length distribution—experiment 1 (30M_enhuj_v2_4): English–Hungarian–Japanese distribution. Figure 9: Text length distribution—experiment 1 (50M_enhuj_v2_2): English–Hungarian–Japanese distribution. We investigated three different project types during the experiments [1], and based on the results, we validated the outcomes with a multitype dataset as presented in [47]. The training category was layered into (1) English; (2) Hungarian; and (3) multilanguage trainings, in which Japanese, English, and Hungarian languages were all present in the dataset. During our experiments, we worked with large datasets as well, but based on the initial result, these experiments were frozen because of the expected running times and are to be continued in high-performance computing (HPC) environment. The experiments were executed in the following dedicated environments: Config 1 PC with the following configuration: MBO Gigabyte Z390 Aorus Pro, CPU INTEL Core i7-8700K 3.7 GHz 12 MB LGA1151, DDR4 32 GB 3600 MHz Kingston HyperX Predator Black CL17 KIT2, VGA MSI RTX 2080 Ventus 8 GB, SSD M.2 SAMSUNG 970 Pro 1 TB, Corsair RMx (2018) 750 W Modular 80+ Gold) [47]; The second configuration: VGA MSI RTX 2080 Ventus 8 GB (2x); and in the last phase we used a configuration with VGA MSI RTX 2080 Ventus 8 GB (6x). For the research, we used both generated and unified vocabulary types [47]. Table 1 shows the adaptive vocabulary selection used in the experiments conducted in the feasibility study, while Table 2 represents the validation phase with a wide variety of datasets and dictionaries. Table 3 shows the details of the \"15M_enhujp_v2_1\", \"30M_enhujp_v2_4\", and \"50M_enhujp_v2_2\" projects. In the experiments, we used the SVTR [42,47] and CRNN [47,55] models. These figures are accessible to the public; see the Data availability statement. The experiments were conducted with the SVTR [42,47] and CRNN [47,55] models.