## 4 Experimental Results and Discussion
### LP Characters Recognition Results Analysis
#### 4.2.3 Impact of FG-Polarity Matching Module  
We explored the proposition that, in terms of foreground polarity, data uniformity enhances the network recognition performance. Our focused work is for multinational VLPs, and we know that different country LPs have different foreground polarities, even one country LPs may have different FP. For this purpose, we propose and integrate polarity matching module before feeding the image to multichannel convolutional neural network, that checks and inverts the foreground polarity of an image if needed, as we already have FP information that is extracted by using foreground/background classification framework at segmentation stage. The foreground polarity matching module works as follows  
\[I_{\text{FP}}(x,y)\ =\ \left\{\begin{array}{l}I_{\text{FP}}(x,y)\ if\ FP=\ Dark\\ I_{\text{FP}}(x,y)\ if\ FP=\ Bright\end{array}\right.\]  
The FP matching module is integrated between multi-channel input and multi-channel CNN network module to rectify the mismatch of foreground polarity and gives more accurate recognition results as compared to the previous stage. A data normalization module is also used to make an output feature vector in standardized form. The total 96% recognition accuracy is achieved after the integration of these modules in the proposed framework.  
For better understanding, we graphically present the performance comparison of every module for all targeted countries separately, as shown in Figures 16 and 17. Figure 16 shows the cumulative accuracy of every integrated module while Figure 17 only demonstrates the percentage increase in accuracy that every module contributes in the proposed system. It is observed that cumulative recognition performance enhances from 86.25% to 96% for our proposed network with the combination of different modules at different levels, as shown in Figure 18. Base represents the result achieved by the base network while Level 1, Level 2 and Level 3 represent the output of layer aggregation, multi-channel CNN and FG polarity matching modules respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{5}{c}{**Recognition Accuracy, \%**} \\ \hline Country & CNN\({}_{\text{B}}\) & CNN-LA & M\({}_{\text{MC-CNN-LA}}\) & M\({}_{\text{MC-CNN-LA}}\) & M\({}_{\text{PM}}\) \\ \hline Australia & 89.67 & 91.55 & 92.64 & 97.23 \\ Canada & 78.76 & 84.62 & 87.99 & 92.77 \\ England & 85.12 & 88.43 & 90.47 & 93.39 \\ Mexico & 93.47 & 96.19 & 96.53 & 98.86 \\ Pakistan & 83.48 & 90.89 & 93.92 & 98.71 \\ Europe & 90.78 & 93.76 & 94.16 & 97.86 \\ UAE & 88.11 & 91.25 & 92.53 & 93.04 \\ USA & 80.64 & 85.69 & 88.76 & 96.44 \\ \hline Total & 86.25 & 90.30 & 92.13 & 96.04 \\ \hline \end{tabular}
\end{table}
Table 5: Performance comparison of different modules of proposed recognition network.  
A comparison of the proposed method with already existing works is also provided. Five existing state-of-the-art methods have been implemented in MATLAB and their performance is recorded on test dataset. These existing methods are compared with the proposed method in terms of recognition  
Figure 16: Graphical representation of integrated modules overall performance with base structure.  
Figure 17: Graphical representation of individual modules contribution for every targeted country.  
Figure 18: Graphical behavior of accumulative rise in recognition accuracy for proposed modules.  
accuracy and computational time and results are presented in Table 6. It has been observed that the proposed method outperforms all the existing methods by achieving 96.04% recognition accuracy. Furthermore, the proposed method is faster, in terms of computational time, than all tested methods except the method in [21]. Although the method in [21] has little better computational time than the proposed method, its accuracy is almost 12.5% less than the proposed method.