## 4 Experiments
### Challenges and Mitigation Strategies  
Various challenges such as time-consuming procedures, unforeseen issues, and inconsistencies in the observed behaviors characterized the experimental phase. The challenges and constraints faced throughout the execution of PaddleOCR are highlighted below:  
1. _Single-line and/or multiline approach_: One of the important technical questions was the capacity of PaddleOCR to handle multiple lines of text. During the experimental phase, it was seen that the system exhibited restricted multiline capabilities under certain conditions. Specifically, this limitation became apparent when the vocabulary size was sufficiently short (ranging from 100 to 200 characters) and the training examples were meticulously chosen. During multilingual experimentation, it was out of scope to provide sufficient time for the manual review of training data that involved character-based multiline analysis. As a result, we opted for the single-line solution offered by PaddleOCR.
2. _Vocabulary generation_: PaddleOCR currently lacks an in-process vocabulary-generating tool. The process of vocabulary formation necessitates manual management. There are two methods by which a lexicon or dictionary can be generated for training. (1) One potential approach is the development of a cohesive and standardized lexicon, commonly referred to as a \"golden\" vocabulary, which is meticulously overseen and curated by knowledgeable human specialists. (2) An alternative approach involves generating language based on the training data. With this approach, we can guarantee that the vocabulary only includes characters that are found in the training sample, eliminating any extraneous characters.
3. _SVTR vs. CRNN_: During the course of the investigation, PaddleOCR introduced a novel iteration of its software (PaddleOCRv2.5.0 with PP-OCRv3) [56],which incorporates the SVTR paradigm. According to PaddleOCR, the newly proposed model architecture exhibits superior performance compared to its predecessor, CRNN. Numerous experiments were conducted on the novel model architecture, consistently revealing its inferior performance compared to the capabilities exhibited by the prior architecture. Following a thorough analysis of the results obtained using the SVTR, it was determined that reverting back to the utilization of CRNN models is the most appropriate course of action. In the high-performance computing (HPC) environment, we intend to conduct a comprehensive examination of both methodologies.
4. _Word spacing and segmentation issues_: Japanese texts had improper word spacing during the experiments. Using a tiny-segmenter Japanese text tokenizer improved the performance. Setting up tiny segmenters and tokenizing/segmenting Japanese or Chinese requires expertise. Japanese vs. Unicode character coding is another issue. High-level OCR engines work in a straightforward manner. The model looks at each character to identify it. This may lead to errors. Imagine identical characters with different character codes. In this example, the model will recognize the character as one of two options during training. If this character belongs to another character code, the model returns the information that it made a mistake because it is not that character. This causes training and inference mistakes for the model. The solution is to identify these homoglyphs and change them all to the selected character. For Latin characters, dictionaries solve the problem, but for Japanese or Chinese literature, homoglyphs require further study and depend on the domain. A custom dataset-generating tool handled homoglyphs that way. The 'normalizeHomoglyps' attribute works with a predefined homoglyph table to handle characters/symbols that seem similar but have various character encodings for text sources. The latest version provides automatic Unicode normalization for each text source using the NFKC method.