## 5 Experimental Analysis
### Comments on Misclassifications  
To find out why misclassifications were occurring, the focus was needed on the worst-performing classes for each of the datasets. This was the reason for which we decided to calculate class-wise performance in the first place. Table 11 represents the two worst-performing classes for each of the datasets while applying the proposed architectures. If noticed closely, the two worst-performing class accuracy is high for datasets 1, 2, and 6 as these datasets have 10, 50, and 7 classes. Two worst-performing class accuracy is low for other datasets where 171, 199, and 256 classes are under consideration. As the number of test samples is higher in these datasets, a few classes with low accuracy does not hurt the overall accuracy. Additionally, some classes have a very small number of test samples i.e., 10-20 test samples. For these classes, a small number of misclassifications will cause low class-wise accuracy. For example, if 2 out of 10 samples are misclassified, the class-wise accuracy becomes 80%. On the other hand, another class with 2 misclassifications out of 100 samples will produce 98% class-wise accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset Name** & **Voting Method** & **SoftMax Averaging Technique** \\ \hline Dataset-1 & 98.21\% & **99.01\%** \\ Dataset-2 & 98.03\% & **98.97\%** \\ Dataset-3 & 97.84\% & **98.35\%** \\ Dataset-4 & 95.65\% & **96.07\%** \\ Dataset-5 & 94.93\% & **95.89\%** \\ Dataset-6 & 95.17\% & **97.79\%** \\ Dataset-7 & 96.59\% & **97.82\%** \\ Dataset-8 & 94.96\% & **95.71\%** \\ Ekush Basic & 97.91\% & **98.36\%** \\ Ekush Numerals & 98.57\% & **99.13\%** \\ BanglaLekha Basic & 94.55\% & **96.09\%** \\ BanglaLekha Numerals & 97.47\% & **98.11\%** \\ NumtaDB Numerals & 96.64\% & **97.50\%** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of performance among SoftMax averaging technique and voting method for the considered datasets.  
From Table 11, it can be observed that some of the classes are frequently victims of misclassification. We have two observations or comments on these misclassifications. First, some classes are similar in terms of curves and looks. That is why misclassifications happened frequently as the models become confused while recognizing these extremely similar classes. Figure 11 presents some classes of this type.  
Secondly, for compound characters, some characters had more than one writing fashion. For some characters, the writing fashions were similar and for some, the writing fashions were different. Hence, the model struggled whether these writing fashions were merged into one class or considered to be separate classes. Figure 12 represents some classes of this type. Moreover, there are some samples in the test set which are so noisy that even human eyes cannot predict the classes correctly.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Name** & **Number of Classes** & **Class Number** & **Class Accuracy** \\ \hline \multirow{2}{*}{Dataset-1} & \multirow{2}{*}{10} & 6 & 96\% \\  & & 9 & 96\% \\ \hline \multirow{2}{*}{Dataset-2} & \multirow{2}{*}{50} & 30 & 91\% \\  & & 44 & 95\% \\ \hline \multirow{2}{*}{Dataset-3} & \multirow{2}{*}{171} & 19 & 70\% \\  & & 139 & 86\% \\ \hline \multirow{2}{*}{Dataset-4} & \multirow{2}{*}{171} & 141 & 69\% \\  & & 59 & 72\% \\ \hline \multirow{2}{*}{Dataset-5} & \multirow{2}{*}{199} & 180 & 65\% \\  & & 141 & 69\% \\ \hline \multirow{2}{*}{Dataset-6} & \multirow{2}{*}{7} & 3 & 91\% \\  & & 6 & 96\% \\ \hline \multirow{2}{*}{Dataset-7} & \multirow{2}{*}{256} & 40 & 70\% \\  & & 182 & 74\% \\ \hline \multirow{2}{*}{Dataset-8} & \multirow{2}{*}{256} & 123 & 60\% \\  & & 237 & 63\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: Two Worst-Performing Classes for 8 Datasets.  
Figure 11: Examples of extremely similar classes for different datasets.