## 1 Introduction
It stresses the need for standardized testing methods and standards that take into account the difficulties that come with mixed languages and different types of language so that the performance evaluations of OCR systems can be trusted and compared. 5. RQ 5: What are the effective adaptation and fine-tuning techniques for deep learning models, specifically SVTR and CRNNs, to address the challenges of multilanguage OCR? Furthermore, what strategies can be employed to optimize the performance of these models for real-time on-screen OCR in collaborative environments? The purpose of this inquiryis to examine the process of adapting and optimizing deep learning models, namely SVTR and CRNN, for the purpose of performing multilanguage OCR jobs. The statement recognizes the necessity of adapting models to suit the complexities of various languages and the demands of real-time, collaborative on-screen OCR applications. We propose Hypothesis 1 (H1) about RQ1: Adding language-specific context modeling techniques to a synthetic training dataset will have a considerable effect on the multilanguage OCR system's accuracy and ability to adapt to different contexts. This is especially true for languages like Japanese and Chinese, which have a lot of complex contextual dependencies, as well as the Latin and Anglo-Saxon language families. It is suggested in H1 that adding language-specific contextual information to the creation of synthetic training data will improve the accuracy of optical character recognition (OCR) for languageslike Chinese and Japanese, which are sensitive to context. Additionally, this approach is expected to maintain a high level of performance for languages belonging to the Latin or Anglo-Saxon language families. The assumption is made that employing a context-aware strategy will effectively reduce the influence of linguistic variations, thereby leading to improved language recognition capabilities across different languages. As a result of RQ2, Hypothesis 2 (H2) is true: Using a variety of fonts, writing styles, and document layouts when creating fake training data will make the multilanguage OCR system more resilient and adaptable, allowing it to handle the visual differences seen in different languages and document formats. Adding different fonts, writing styles, and document layouts to fake datasets will help an optical character recognition (OCR) system better handle the different visual features found in multilingual and multimodal documents. With this method, it is postulated that the OCR system's capacity for effective generalization can be enhanced through exposure to a wide range of visual features during training. Similarly, Hypothesis 3 (H3) is deduced from RQ3: The utilization of language-aware techniques for the generation of labels and quality control during the creation of multilanguage OCR training datasets will yield enhanced accuracy and linguistic precision in annotations. Consequently, this will lead to improved OCR performance in languages that possess distinct linguistic characteristics. The argument for H3 comes from the idea that using language-specific methods to make labels and make sure that they are accurate will result in training datasets that accurately reflect the unique features of each language. The assumption, based on the experiments, is that the utilization of exact linguistic annotations will result in improved accuracy in recognizing languages that possess different linguistic characteristics. The fourth idea, Hypothesis 4 (H4) for RQ4, suggests that it will be easier to test AI-powered multilanguage real-time OCR systems in a way that is both rigorous and meaningful if there are standardized testing methods and standards that take into account the complexity of synthesized languages and how different languages work. This, in turn, will enhance their reliability and comparability.