## 4 Experiments  
In the conducted trials, both the CRNN and SVTR models were employed. CRNN is a prevalent and extensively used modeling approach in image-based text recognition. Extracting advantageous features involves the consecutive application of convolutions and max-pooling processes. Subsequently, the inputs mentioned above are introduced into the recurrent unit to record and analyze the temporal information effectively. The terminology \"v2\" (see Tables 1-5) was assigned to these model architectures throughout the trials, as Paddle OCR2 utilized them for text recognition. The SVTR approach initially breaks  
Figure 5: Distribution of text lengths in the train dataset. Figure 6: Character number distribution. down image text into smaller patches called character components. Subsequently, there is a recurring implementation of hierarchical steps, including mixing, merging, and/or combining components at the component level. Global and local mixing blocks were developed to detect intercharacter and intracharacter patterns, resulting in a perception of character components at many levels of granularity. The terminology \"v3\" (see Tables 1-5) was assigned to these model designs throughout the studies, as PaddleOCR3 specifically employed this model variant for text recognition purposes. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Name** & **Vocabulary Name** & **Size** & **Type** \\ \hline
15M\_enhujp\_v2\_1 & training9-200\_vocab\_min200.txt & 2737 & Generated \\
30M\_enhujp\_v2\_4 & dashboard\_vocab.txt & 1441 & Generated \\
50M\_enhujp\_v2\_2 & training\_min250\_nQA.txt & 3964 & Generated \\
3M\_dashboard\_eng\_V1 & training9-200\_vocab\_min9500.txt & 803 & Generated \\
3M\_dashboard\_hun\_v1 & 186k\_extended\_vocab.txt & 112 & Unified \\
3M\_dashboard\_enhujp\_v2\_1 & jpn\_latin\_dict.txt & 4444 & Unified \\
10M\_enhujp\_v2\_1 & jpn\_latin\_dict.txt & 4444 & Unified \\
30M\_enhujp\_v2\_1 & dashboard\_vocab.txt & 1441 & Generated \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Vocabularies used to test the approach. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Project** & **Name** & **Vocabulary Name** & **Size** & **Vocabulary Type** \\ \hline OCR\_hun & 155k\_hu\_v2\_2 & 606k\_hun\_vocab.txt & 112 & Generated \\ OCR\_hun & 155k\_hu\_v2\_1 & 606k\_hun\_vocab.txt & 112 & Generated \\ OCR\_enhu & SVTR & extended\_vocab.txt & 201 & Unified \\ OCR\_enhu & CRNN & extended\_vocab.txt & 201 & Unified \\ OCR\_multilang & 5M\_enhujp\_v2\_8 & training\_200\_vocab\_min9500.txt & 803 & Generated \\ OCR\_multilang & 5M\_enhujp\_v2\_6 & training\_200\_vocab\_min200.txt0 & 2737 & Generated \\ OCR\_multilang & 5M\_enhujp\_v2\_5 & training\_200\_vocab\_min9500.txt & 803 & Generated \\ OCR\_multilang & 5M\_enhujp\_v2\_4 & training\_9200\_vocab\_min200.txt & 2737 & Generated \\ OCR\_multilang & 5M\_enhujp\_v2\_3 & jpn\_latin\_dict.txt & 4444 & Unified \\ OCR\_multilang & 5M\_enhujp\_v2\_2 & jpn\_latin\_dict.txt & 4444 & Unified \\ OCR\_multilang & 5M\_enhujp\_v2\_1 & jpn\_latin\_dict.txt & 4444 & Unified \\ OCR\_multilang & 10M\_enhujp\_v3\_1 & jpn\_latin\_dict.txt & 4444 & Unified \\ OCR\_multilang & 5M\_enhujp\_v3\_4 & jpn\_latin\_dict.txt & 4444 & Unified \\ OCR\_multilang & 5M\_enhujp\_v3\_3 & jpn\_latin\_dict.txt & 4444 & Unified \\ OCR\_multilang & 5M\_enhujp\_v3\_2 & jpn\_latin\_dict.txt & 4444 & Unified \\ OCR\_multilang & 4M\_enhujp\_pre\_v3\_1 & jpn\_latin\_dict.txt & 4444 & Unified \\ OCR\_multilang & 4M\_enhujp\_pre\_v3\_1 & jpn\_latin\_dict.txt & 4444 & Unified \\ OCR\_multilang & 4M\_enhujp\_v3\_5 & 4M\_vocab.txt & 4721 & Generated \\ OCR\_multilang & 4M\_enhujp\_v3\_4 & 4M\_min20\_vocab.txt & 3980 & Generated \\ OCR\_multilang & 2M\_enhujp\_v2\_2 & 2M\_min2k\_vocab.txt & 968 & Generated \\ OCR\_hun & 186k\_hu\_v2\_1 & 186k\_extended\_vocab.txt & 112 & Unified \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Vocabularies used for approach validation according to the experiments highlighted in [47].