## 3 Additional Experiments  
To verify the universality and usefulness of the proposed approach, some additional experiments were performed for a larger dataset of images, being the results of application of 12 binarization methods for the document images captured by mobile phone cameras. The algorithms selected during these experiments were [PERSON] [1], [PERSON] [12], [PERSON] [16], [PERSON] [17], [PERSON] [18], [PERSON] [25], [PERSON] [15], [PERSON] [26], [PERSON] [27], [PERSON] [28], WAN [29], and ISauvola [30]. As the source data (nearly 13 GB) for the binarization algorithms, the images from the SmartDoc-QA dataset available at [[https://zenodo.org/records/5293201](https://zenodo.org/records/5293201)]([https://zenodo.org/records/5293201](https://zenodo.org/records/5293201)) (accessed on 12 October 2024) were chosen due to the presence of several single and multiple distortions [31]. The SmartDoc-QA dataset contains two subsets with 2130 images of 30 documents each, captured using Nokia and Samsung smartphones, respectively, under varying conditions (subject to motion blur, out-of-focus blur, differing light conditions, and perspective angles), together with ground-truth data containing text transcriptions (the expected OCR results). The three types of documents present in the dataset consist of contemporary documents, old administrative documents, and shop receipts (10 samples per type). Both subsets have different size of images: 3264 \(\times\) 2448 pixels for Nokia and 4128 \(\times\) 3096 pixels for Samsung. For the verification of the proposed approach, all images from the SmartDoc-QA dataset were binarized using the 12 abovementioned algorithms, and mutual comparisons were conducted similar to the WEZUT Dataset used in previous experiments. Nevertheless, since most of the images contain relatively large background areas that produce solid white or black fragments of binary images, depending on the individual thresholding method, one cannot expect as high mutual similarities as for the WEZUT Dataset containing document images without large background areas. An illustration of this phenomenon is presented in Figure 7, where the sample image from the SmartDoc-QA dataset is shown together with its  
Figure 6: A sample image from the WEZUT OCR Dataset: original image (**a**), after Sauvola binarization (**b**), after Feng binarization (**c**), and after NICK binarization (**d**), together with respective values of three combined metrics and Levenshtein Distances. results of binarization using three various methods. The easily noticeable differences in the background areas of the binary images are the main reason for the decreased correlations between the results of the binary image quality assessment and the Levenshtein Distance for this extended dataset. Furthermore, to verify the universality of the proposed approach, apart from the Tesseract engine, the other OCR software implemented in Python by JaidedAI, known as EasyOCR (version 1.7.2), was used. Its code is available on GitHub at [[https://github.com/[PERSON]/EasyOCR](https://github.com/[PERSON]/EasyOCR)]([https://github.com/[PERSON]/EasyOCR](https://github.com/[PERSON]/EasyOCR)) (accessed on 12 October 2024). Therefore, the binary IQA metrics were computed mutually for each binarization algorithm and the 11 others, and then their correlations with the Levenshtein Distances (LD) obtained after applying the Tesseract and EasyOCR engines independently were determined. Finally, the results achieved for various binarization methods were averaged, leading to 2130 average correlation coefficients for each of two subsets (Nokia and Samsung) between the IQA metric and the LD values calculated separately for two OCR engines. The correlations obtained for individual metrics are presented in Table 3, where the highest values may be observed for F-Measure and pseudo-F-Measure, similar to previous experiments conducted for the WEZUT Dataset.