# Synthetized Multilanguage OCR Using CRNN and SVTR Models for Realtime Collaborative Tools
## 1 Introduction  
In recent years, thanks to remote diagnosis using collaborative tools [1], multilingual joint working sessions [2] have started to take place in several domains, including health care. One of the challenges in establishing noninvasive, safe, and distant cooperation models, which will be able to provide a solution that enables sharing documents and presentations on the screen in real time [2] to increase the effectiveness of these noninvasive, safe, and distant cooperation models. Traditionally, optical character recognition (OCR) solutions [3; 4; 5; 6; 7] have not been successful when a combination of languages, dialects, technical linguistic layers, or different levels or skills of technicians are involved in the process [8; 9]. There are also risks of misinterpretations caused by incorrect translations or the interpreters' lack of domain knowledge. Therefore, artificial intelligence (AI) must also improve [10] the OCR part of this technological pipeline to avoid misunderstandings [11; 12].  
Real-time translation pipelines [2] now include OCR [13; 14; 15] as one of their central pillars, which helps solve all translation and interpretation problems. Our research results with the technological pipeline (see Figure 1)--supporting the collaborative videoconferencing tools and platforms--can be applied to healthcare and sports safety.  
The originality of our approach lies in the fact that it offers a real-time synthesized multilanguage detection solution not only for the various languages spoken by humans [16; 17; 18], but also for their sublayers as well as a mixture of technical languages in the wording of texts. This is accomplished through the utilization of artificially created vocabulary as well as a customized method for the generation of training data. The data extraction could be added to neural translation engines [18] to shorten the communication gaps.  
Using synthesized language preparation, this study evaluates the feasibility of synthesized language preparation and its adaptability to machine learning (ML)-based OCR engines [19; 20; 21; 22; 23] designed to provide efficient solutions for machine learning-based neural translation engines. It validates the effectiveness of analysis for preparing and adapting a synthetic language based on machine learning.  
A significant contribution of this paper is how it approaches the problem of developing an artificial intelligence pipeline for interaction with healthcare professionals to facilitate collaborative videoconferencing solutions [2] based on the research presented in this document.  
The purpose of the research in both phases was to conduct limited validation-type experiment rounds, summarize their findings, simultaneously measure, compare, and analyze the results, and develop specific requirements concerning the effectiveness of the input data and the effectiveness of the preparation process [2]. These experiments are designed to demonstrate that achieving high accuracy is possible with a tuned model and a suitable algorithm to generate vocabulary and training datasets. In addition, by making use of AI models that have already been established in other industries, we can make predictions with a high degree of accuracy to cover the use cases of \"videoconference-based collaborative tools\" [1; 2; 9; 24], which can be used for further modeling and translation into  
Figure 1: Near real time synthesized language translation pipeline for distant cooperation.  
real life. Attendees of video diagnostics could benefit from these models as a component of a more collaborative communication pipeline.  
This study takes a unique and cutting-edge approach to remote diagnostics by repurposing pre-existing AI programs and ML algorithms from various other disciplines. It does so through utilizing collaborative use cases, which result in time and resource savings, as well as an increase in the efficiency of multilingual collaboration solutions [2].