## 1 Introduction  
The area of remote diagnosis has undergone a transformational change thanks to the power of cutting-edge collaboration tools, ushering in a new era of multilingual cooperative working sessions that span many sectors, most notably health care [1]. The quest for the establishment of non-invasive, secure, and remote cooperation models is gaining momentum, the apex of which is to find a solution that facilitates real-time sharing of documents and presentations, thereby amplifying the efficacy of these cooperative models [2]. Previously, the application of optical character recognition (OCR) solutions posed a substantial challenge when mixcode [3, 4], multiple or synthesized languages (a mixture of context-sensitive languages and Anglo-Saxon and Latin language families), dialects, or varying degrees of technical linguistic proficiency were involved in the process [5]. Furthermore, the risks associated with misinterpretations induced by incorrect translations and deficiencies in the domain knowledge of interpreters cannot be understated. This paper outlines and tackles five research questions (RQs), which collectively guided the investigations into the optimal preparation of training datasets for AI-supportedmultilanguage real-time OCRs in collaborative on-screen solutions. The RQs address key challenges in the OCR technology research domain, acknowledging the complexities introduced by synthesized languages, contextual dependencies, and linguistic diversity within the Latin and Anglo-Saxon language families. The RQs with their specific rationales and contributions are enumerated below:  
1. RQ 1: What are the most effective strategies for creating a synthetic training dataset that accurately captures the intricacies of multilanguage OCR, particularly when dealing with context-sensitive languages alongside Latin and Anglo-Saxon language families? Rationale: This RQ addresses the fundamental challenge of preparing an optimal training dataset that accommodates a diverse set of languages, including those with varying contextual dependencies. Based on our research experiments conducted using a convolutional recurrent neural network (CRNN) and scene text recognition with a single visual model for scene text recognition (SVTR) models with PaddleOCR, the need for specific strategies to capture nuances in languages such as Chinese or Japanese (context-sensitive) and English/Hungarian (Anglo-Saxon/Latin) was recognized within a single OCR system for collaborative on-screen solutions. 2. RQ 2: How can the variability of fonts, writing styles, and document layouts be systematically incorporated into the generation of synthetic training data to ensure the robustness of a multilanguage OCR system? Rationale: This question delves into the critical issue of accommodating diversity in fonts, writing styles, and document layouts within a synthetic dataset. Addressing this challenge is crucial for training an OCR system that can effectively handle visual heterogeneity across languages and document types. 3. RQ 3: What are the optimal techniques for metadata-based quality control in the context of a multilanguage OCR training dataset, and how can these techniques be applied efficiently for languages with distinct linguistic features? Rationale: This question focuses on the often overlooked aspect of the advanced level of quality control in multilanguage OCR training datasets. It emphasizes the need for tailored techniques that consider the linguistic peculiarities of each language, ensuring high-quality annotation methods across diverse linguistic backgrounds. 4. RQ 4: What methods and standards can be set up to thoroughly test the precision, dependability, and effectiveness of AI-powered real-time multilanguage OCR systems, taking into account the complexity of synthesized languages and different language families? Rationale: This question addresses the crucial aspect of evaluation in the context of multilanguage OCR systems.