## 9 Conclusions
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Topic** & **Classic AI Infrastructure** & **HPC Cluster Environment** \\ \hline \multirow{4}{*}{Computational Power/Resources} & In classic AI infrastructure, the user is limited by the computational resources available on a single machine or in a small cluster. Generating synthetic training data for multiple languages with a large dataset can be computationally intensive and time-consuming. & HPC clusters offer significantly more computational power, allowing the user to efficiently generate larger and more complex synthetic datasets for multilanguage OCR. \\ \hline \multirow{4}{*}{Scalability} & Classic AI infrastructure may lack the scalability required to efficiently simultaneously generate synthetic datasets multiple languages. The user may need to create separate work flows for each language, which can be inefficient. & HPC clusters are designed for parallel processing, which means the user can distribute the workload across multiple nodes, enabling simultaneous dataset generation for multiple languages. This enhances efficiency and reduces time requirements. \\ \hline \multirow{4}{*}{Data Handling/Volume} & Managing and storing large volumes of synthetic data for multiple languages can be challenging in a traditional setup. The user might encounter storage limitations and data management issues. & HPC environments provide advanced data storage and management capabilities. The handling and storage of large volumes of synthetic data are streamlined. \\ \hline \multirow{4}{*}{Parallelism/Distributed Computing} & Implementing parallelism for data generation is more complex in classic infrastructure, making it harder to exploit multicore CPUs or GPUs efficiently. & HPC clusters are well-suited for distributed computing, enabling the user to distribute the dataset generation tasks across multiple nodes, which is especially beneficial for the generation of synthetic datasets for multiple languages. \\ \hline \multirow{4}{*}{Resource Management} & \multirow{4}{*}{—} & HPC environments offer sophisticated resource management tools and job schedulers, allowing the user to optimize resource allocation and utilization for dataset generation tasks. \\ \cline{1-1} \cline{2-2}  & & HPC clusters are well-suited for distributed computing, complex in classic infrastructure, making it harder to exploit multicore CPUs or GPUs efficiently. & HPC clusters are well-suited for distributed computing, enabling the user to distribute the dataset generation tasks across multiple nodes, which is especially beneficial for the generation of synthetic datasets for multiple languages. \\ \hline \multirow{4}{*}{Resource Management} & \multirow{4}{*}{—} & HPC environments offer sophisticated resource management tools and job schedulers, allowing the user to optimize resource allocation and utilization for dataset generation tasks. \\ \cline{1-1} \cline{2-2}  & & HPC environments offer sophisticated resource management tools and job schedulers, allowing the user to optimize resource allocation and utilization for dataset generation tasks. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of open challenges comparison: classic AI infrastructure vs. an HPC environment. The key finding is that ML models developed following the methods outlined in this research perform consistently well and accurately. Multilingual/hybrid texts were accurately detected by the ML models. The highest precision scores were attained in the following experiments: \"30M_enhujp_v2_4\", with 98.76% \"30M_enhujp_v2_1\", with 95.87%; and \"50M_enhujp_v2_2\" with 94% accuracy. In summary, the proposed methodology justifies the notion that optimal dataset preparation in real-time multilanguage or hybrid language training requires a well-defined pipeline with domain expertise. Following the proposed methodology will result in enhanced model performance, providing broader language coverage and adaptability to language variations at the same time.