## 6 Discussion
### Limitations of the Current Approach  
While this research endeavors to address significant challenges in multilanguage OCR and synthesized languages for collaborative on-screen OCR solutions (especially in health care and remote diagnosis), there were localized limitations that underscore the complexity of the task. The specific constraints that necessitate thorough examination are as follows:  
1. _Synthesized Languages_: Investigating synthesized languages (like health language, legal language, and IT language), which may not have well-established linguistic resources or standardized writing systems, introduces an additional layer of complexity. The availability of reliable linguistic references and textual corpora for such languages may be limited, potentially hampering the completeness and authenticity of the synthetic training data.
2. _Computational Resources_: This research involves computationally intensive tasks, including the generation of synthetic data and the training of deep learning models. Depending on the scale of the dataset and the complexity of the models, substantial computational resources or a high-performance computing (HPC) environment is required. Access to such resources may impose constraints, particularly for researchers with limited access to HPC environments.
3. _Generalization_: while this research seeks to optimize training datasets and models for multilanguage OCR, the extent to which the findings can be generalized to various OCR applications and domains may be limited. Factors such as document types, writing styles, and use cases may influence the transferability of the results.
4. _Evaluation Challenges_: Establishing robust, metadata-based evaluation methodologies and benchmarks for multilanguage OCR systems that consider the complexities of synthesized languages is a formidable task. The creation of evaluation frameworks that adequately reflect real-world scenarios (like cross-domain or field cooperation) and effectively assess system performance across multiple languages is an ongoing challenge.
5. _Data Annotation_: The generation of high-quality, linguistically accurate annotations for synthetic training datasets is a resource-intensive and time-consuming endeavor. The practicality of creating comprehensive annotations that encompass diverse linguistic features across multiple languages and/or domains (law, IT, health care, sports, and industry), especially for context-sensitive and synthesized languages, may present significant logistical challenges, even in HPC environments.
6. _Language Diversity Coverage_: One prominent limitation pertains to the diverse nature of the languages involved. While this research aims to address multilingual challenges, each language possesses unique characteristics, scripts, and linguistic nuances. Consequently, the development of a single, universally applicable methodology for training dataset preparation may be challenging. The need for language-specific considerations may introduce complexity and complicate the generalization of findings across languages. Ichuding Japanese language and synthesized language alongside Latin and Anglo-Saxon languages can limit the vocabulary to 5000-6500 characters if the language is limited to the IT domain, but in the case of the health domain, the vocabulary will have include than 10,000 characters.  
Acknowledging these limitations is essential to provide a balanced perspective and guide future research efforts in the demanding and dynamic field of OCR technology.