## 4 Conclusions and Further Research  
The proposed method for predicting the quality of OCR results, based on the application of image quality assessment of binary images as the aggregated mutual similarity indexes makes it possible to prevent the necessity of time-consuming text recognition, particularly for low-quality images. Due to a much faster execution of adaptive binarization, which may be efficiently executed in parallel also using integral images to speed up the computations, such as, e.g., in the Bradley method [13], after additional quality assessment of the results, mobile device users may obtain the information about the necessity for the acquisition of another image. Since some OCR engines work as cloud services, there is no necessity of sending various binary versions of the input image to the cloud using the proposed approach. In this case, the transmission time may be considered as an additional constraint, dependent on the network properties. The proposed preprocessing may be useful, particularly for devices where the execution of the OCR engine would be troublesome, e.g., due to memory limitations. The main assumption is the most appropriate preprocessing of the input image before a single run of the OCR engine leading to the best possible text recognition results.  
The experimental verification of various binary IQA methods confirmed the usefulness of the F-Measure and pseudo-F-Measure for this purpose, as well as the advantages of their nonlinear combinations, leading to the linear correlation between the proposed combined aggregated mutual similarity and Levenshtein Distances of nearly 0.91 for the WEZUT Dataset. Noticeably smaller correlation values observed during the additional verification for the larger dataset are caused by the presence of some differences in the binarization of background areas using individual thresholding methods. Since most of the metrics are based on the mutual comparison of pixels, these differences influence the binary image quality assessment results regardless of the final OCR effects, decreasing the overall correlation of the binary IQA and the OCR results. Nevertheless, these experiments fully confirmed the choice of elementary metrics, as well as the validity and usefulness of the proposed approach. During further research, it is planned to develop even better models utilizing the combination of metrics also for short video sequences.  
Conceptualization, M.K. and K.O.; methodology, M.K. and K.O.; software, M.K., P.L. and K.O.; validation, M.K. and K.O.; formal analysis, M.K. and K.O.; investigation, M.K. and K.O.; resources, M.K., P.L. and K.O.; data curation, M.K., P.L. and K.O.; writing--original draft preparation, M.K. and K.O.; writing--review and editing, K.O.; visualization, M.K. and K.O.; supervision, K.O.; project administration, K.O.; funding acquisition, K.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to the use of external datasets as the input data for creating an extended dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**OCR Engine** & \multicolumn{2}{c}{**Tesseract**} & \multicolumn{2}{c}{**EasyOCR**} \\ \hline
**Quality Metric** & **Nokia** & **Samsung** & **Nokia** & **Samsung** \\ \hline \(CM_{1}\) & 0.4839 & 0.5002 & 0.5569 & 0.6002 \\ \(CM_{2}\) & 0.4855 & 0.5003 & 0.5669 & 0.5996 \\ \(CM_{3}\) & 0.4892 & 0.5005 & 0.5734 & 0.6037 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Averaged Pearson’s linear correlation coefficients (PLCCs) of the combined metrics with the Levenshtein Distance for the considered images from the SmartDoc-QA subsets.  
Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.