## 4 Model Training and Discussion of the Results Obtained
\begin{table}
\begin{tabular}{|c c c|} \hline \hline
**Artifact** & \multicolumn{2}{c}{**Generator Parameters**} & **Description** \\ \hline
**Gaussian Noise** & \(\text{Image}_{\text{output}}=\text{Image}_{\text{Input}}+\text{Norm}(\ \mu,\sigma)\) & **Norm is a Gaussian random generator with** \\  & where \(\sigma\in[0,10]\), \(\mu\in[-10,10]\) & **mean value \(\mu\) and standard deviation \(\sigma\).** \\ \hline \hline
**Speckle Noise** & \(\text{Image}_{\text{output}}=\text{Image}_{\text{Input}}*(1+\text{Norm}(0, \sigma))\) & **Norm is a Gaussian random generator with** \\  & where \(\sigma\in[0,0.04]\) & **mean value 0.** \\ \hline
**Salt and Pepper Noise** & \(\text{Let define}\ \text{S}\in\mathbb{Z}^{m,\ n}\text{and}\ \text{P}\ \in\mathbb{Z}^{m,\ n}\text{ where }:\) & **S and P are two matrices in which the** \\  & \(\text{m}=\text{input image width}\) & **defines can have either 0 or 1. The matrix S** \\  & \(\text{n}=\text{input image height}\) & **defines white pixels in the image, and the** \\  & \(\text{s}_{\text{i}\text{j}}\in\{0,1\}\), \(\text{p}_{\text{i}\text{j}}\in\{0,1\}\) & **matrix P represents black pixels in the image.** \\  & \(\sum\text{s}_{\text{i}\text{j}}=\text{o}*\text{t}\) & **They participate together in generating salt** \\  & \(\sum\text{p}_{\text{i}\text{j}}=\text{o}*(1-\text{t})\) & **s is an element of matrix S; p is an element of matrix P; m is number of rows in matrix** \\  & \(\text{t}\in[0.475,\,0.525]\) & **matrix P; m is number of rows in matrix** \\  & \(\text{f}\in[0,80]\) & **either S or P; n is the number of columns in matrix S or P; f and t are random numbers in the defined range which define noise level in percent and both salt and pepper ratios.** \\ \hline
**Contrast** & \(\text{Image}_{\text{output}}=\text{Image}_{\text{Input}}\text{.R}\) & **R is a real value from the given set of values** \\  & where R \(\in[-0.5,\,0.5]\) & **R is a real value from the given set of values** \\ \hline
**Brightness** & \(\text{Image}_{\text{output}}=\text{Image}_{\text{Input}}+\text{R}\) & **The Kernel matrix is defined for convolution** \\  & where \(\text{KernelSize}\ \in\{1\times 1,\,3\times 3,\,5\times 5,\,7\times 7,\,9\, \times 9\}\) & **with the original image** \\ \hline
**Motion Blur** & \(\text{Image}_{\text{output}}=\text{Image}_{\text{Input}}\text{Kernel}_{\text{ Motion Blur}}\) & **The motion blur kernel has one direction of** \\  & where \(\text{KernelSize}\ \in\{1\times 1,\,3\times 3,\,5\times 5,\,7\times 7,\,9\, \times 9\}\) & **45 degrees** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Different parameter settings for our three different datasets generating modules. Regarding the evaluation metrics for testing with OCR, we used two metrics which are generally used for assessing text recognition performance: the character recognition accuracy (CRA) and the word recognition accuracy (WCA). The CRA is the percentage of the number of characters recognized correctly per total number of characters, and the WCA is the percentage of the number of words recognized correctly. The related studies show that the WCA metric is generally used to compare text recognition performance of various schemes [74; 75], therefore we used this metric to show the performance of our model. The effect of newly introduced components such as blur and Gabor filters are examined based on the reference model of [PERSON] et al. [71]. Each of the new filter arrays are being added/removed (e.g., Gabor, blur) from the base model to show the performance based on the absence/presence of the respective component (ablation study). The newly introduced component will stay if it can improve the model behavior. The next two sub-sections (Sections 4.1 and 4.2) comprehensively explain and discuss the performance results of the first module and those of the second module of Figure 3.