## 4 Results and Discussions
### Discussions  
#### 4.2.1 Feature-Wise Discussion  
This section covers a detailed discussion on the strength of features related to various datasets, as mentioned in Table 6.  
Referring to Table 6, it can be observed that in the individual category (Dataset 1 to 4), the features received from VGG-19 Net (Dataset 2) and ResNet-50 (Dataset 3) produced a comparatively good result over the rest of the features, using MLP and SVM  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sl. No.**} & \multirow{2}{*}{**Feature Type**} & **Feature-** & **PCA** \\  & & **Extraction Time** & **Computation** \\  & & **(ms)** & **Time (ms)** \\ \hline
1 & Bior-1.3 & 2.28 & 0.15 & 2.43 \\
2 & VGG-19 & 0.10 & 0.69 & 0.79 \\
3 & ResNet-50 & 0.08 & 0.40 & 0.48 \\
4 & InceptionNet-V3 & 0.17 & 0.40 & 0.57 \\ \hline \multicolumn{3}{c}{Net time} & & 4.27 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Timing details related to the construction of feature-vectors.  
Figure 10: KDE plot for visualizing the separation of features received from (**a**) character images (**b**) fusion-based approach (Dataset 15).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Dataset Type**} & \multicolumn{2}{c}{**Mean Recognition-Time Per Character (Msec.)**} \\ \cline{2-3}  & & **MLP Classifier** & **SVM Classifier** \\ \hline Dataset 15 & 6.38 & 17.27 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Timing details related to character classification.  
classifiers. The features collected from VGG-19 Net were able to produce recognition accuracy of 86.85% on an MLP classifier, and the features received from ResNet-50 were produced 82.66% recognition accuracy on an SVM classifier. These scores were highest in the category.  
In the hybrid category consisting of two types of features (Datasets 5 to 10), the combination of features received from VGG-19 Net and ResNet-50 (Dataset 8) outperformed the rest of the combinations. This combination produced the best results over both the classifiers in this category; the MLP and SVM classifiers produced 95.37% and 93.92% recognition accuracy, respectively, with Dataset 8.  
In the hybrid category with three types of features (Datasets 11 to 14), the fusion of features received from VGG-19 Net, ResNet-50, and InceptionV3-Net (Dataset 14) had an upper edge over the remaining combinations in the category. The features of Dataset 14 were able to produce recognition accuracy of 98.03% and 97.51% over the MLP and SVM classifiers, respectively.  
The state-of-the-art combination of all the four types of features (Dataset 15) produced excellent results over both the classifiers. With the features of Dataset 15, the MLP and SVM classifiers achieved their best results as 98.73% and 98.18%, respectively. The feature's combination of Dataset 15 also justified the choice of wavelet-based features in association with DCNN-based features, as we could see a significant improvement in the results of both the classifiers when moved from Dataset 14 to Dataset 15.  
Dataset 15 has a feature-vector size of 160, which is 84.37% less than that of the character image (i.e., 32 \(\times\) 32 = 1024). This, in turn, reduced the recognition cost of the classifiers significantly.