# Synthetized Multilanguage OCR Using CRNN and SVTR Models for Realtime Collaborative Tools
## 8 Conclusions  
As a result of the experiments discussed in this paper, we localized not only the key parameters of multilingual or hybrid (synthetically generated) language (with extended vocabulary) model training but also the limitations of PaddleOCR, time-stealing processes, contradictory documented behaviors, localizing the room of improvements as well. In the following, we enumerate the findings and outcomes:  
1. Multiline OCR: As a result of the documentation provided by Paddle OCR and the initial research, it was unclear whether PaddleOCR was capable performing of multiline OCR. We validated that the framework has limited multiline capabilities based on dedicated experiments if the vocabulary is small enough (100-200 characters) and the training examples are carefully selected. To handle multiline capabilities, it is necessary to have a GPU-intensive environment and an extensive training dataset in case we have to use a large or/and extended vocabulary (e.g., when we involve Asian languages with special characters in the vocabulary). This means a long training period as well. 2. Vocabulary generation: There is no vocabulary generator tool in PaddleOCR. It is recommended to perform vocabulary generation manually or by using a dedicated tool. 3. SVTR could not outperform CRNN: PaddleOCR released a version of its software during the experimental period that supports the Visual Transformer-based SVTR model. According to PaddleOCR, this new model architecture may perform better than the previous one (CRNN). Several experiments were conducted with the proposed model architecture, which underperformed the previous model architecture each time. Having conducted several experiments, we decided to return to the CRNN model. 4. Character coding: A high-level view of OCR engines [42] shows that their operation is quite simple. Each character is identified by the model based on its appearance. However, some errors may be caused by this process. Let us assume that there are identically shaped characters that have different character codes. In the training process, the model will look at the given character and determine if it is one of the two options. The model will, however, indicate that it made a mistake if this character belongs to another character code. As a result, during the training process, the model will make some errors that will manifest themselves during the inference process. The problem can be solved by identifying similar characters and changing them to the selected symbols. They are referred to as homoglyphs [43; 44; 45]. The use of homoglyph filtering [44] is recommended. The main conclusion of machine learning models for special multilanguages, including languages with artificially made vocabulary, is that they perform consistently with high accuracy. The ML models recognized the multilanguage/hybrid texts with high accuracy. The highest precision scores were achieved with 'Experiment 11' (90.25%), 'Experiment 12' (91.35%), and 'Experiment 14' (93.89%). Conceptualization, A.B., S.M.S. and A.I.C.-V.; methodology, A.B., A.I.C.-V., J.M.-M. and S.M.S. ; software, A.B. ; validation, A.B., L.S. and S.M.S. ; formal analysis, A.B., J.M.-M. and S.M.S. ; investigation, A.B. ; resources, A.B. and S.M.S. ; data curation, A.B. ; writing--original draft preparation, A.B. ; writing--review and editing, A.B. and L.S. ; visualization, A.B. and L.S. ; supervision, A.I.C.-V., J.M.-M. and S.M.S. ; project administration, A.B. ; funding acquisition, A.B. and L.S. All authors have read and agreed to the published version of the manuscript. This study was supported by ITware, Hungary. The work of [PERSON], [PERSON] and [PERSON]-V. was supported by University of Malaga. The work of [PERSON] was supported by the Consolidator Excellence Researcher Program of Obuda University, Budapest Hungary, and the Sapientia Institute for Research Programs, Romania. Not applicable. Data Availability StatementData are available upon request.