## 5 Experimental Analysis
### Comparison with Previous Works  
After measuring the performance of our proposed models, we compared the results with the previous works. Table 7 illustrates the comparison between our proposed approach and previous works. Figure 10 illustrates the comparative results in terms of accuracy for all 8 datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Number of** & **Method/** & **Classification** & **Remarks** \\
**Name** & **Classes** & **Approach** & **Accuracy** & \\ \hline \multirow{4}{*}{Dataset-1} & \multirow{4}{*}{10} & DCNN on PLSS and HOPS [38] & 98.50\% & - \\  & & Support Vector Machine [43] & 98.61\% & - \\  & & CNN [40] & 98.80\% & - \\  & & BengaliNet & **99.01\%** & Proposed \\ \hline \multirow{4}{*}{Dataset-2} & \multirow{4}{*}{50} & Deep CNN [34] & 97.80\% & Custom Test Set, No tuning of learning rate \\  & & Support Vector Machine [44] & 86.10\% & - \\  & & Support Vector Machine [42] & 87.28\% & - \\  & & MobileNet V1 [35] & 98.37\% & Did not consider entire test set \\  & & CNN [40] & 98.56\% & - \\  & & BengaliNet & **98.97\%** & Proposed \\ \hline \multirow{2}{*}{Dataset-3} & \multirow{2}{*}{171} & CNN [40] & 98.12\% & Did not consider the entire train and test sets \\  & & BengaliNet & **98.35\%** & Proposed \\ \hline \multirow{4}{*}{Dataset-4} & \multirow{4}{*}{171} & Hybrid-HOG-based CNN [36] & 92.57\% & - \\  & & DCNN with ELU, dropout [37] & 93.68\% & Did not consider the entire train and test sets \\  & & CNN [40] & 95.70\% & Did not consider the entire train and test sets \\  & & BengaliNet & **96.07\%** & Proposed \\ \hline \multirow{2}{*}{Dataset-5} & \multirow{2}{*}{199} & Hybrid-HOG-based CNN [36] & 92.57\% & - \\  & & BengaliNet & **95.89\%** & Proposed \\ \hline \multirow{2}{*}{Dataset-6} & \multirow{2}{*}{7} & CNN [40] & 97.29\% & Applied augmentation \\  & & BengaliNet & **97.79\%** & Augmentaion+Proposed \\ \hline \multirow{2}{*}{Dataset-7} & \multirow{2}{*}{256} & CNN [40] & 97.42\% & Did not consider the entire train and test sets \\  & & BengaliNet & **97.82\%** & Proposed \\ \hline \multirow{2}{*}{Dataset-8} & \multirow{2}{*}{256} & Two-passed Approach [41] & 87.26\% & Publication by authors of the datasets \\  & & BengaliNet & **95.71\%** & Proposed \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison among results obtained by our proposed BengaliNet and previous approaches. Figure 9: Accuracy vs. learning rate graph for dataset-2. From Table 7, it can be observed that for each formation our proposed approach has outperformed the previous noteworthy works. In the remarks section, we also have provided comments (if required) so that the comparison can be fair. For example, for dataset 7, one research [40] achieved 97.42% accuracy, our proposed architecture has achieved 97.82% accuracy. It does not seem much improvement although there were 11,804 samples and this slight improvement denotes a lot of corrected misclassifications. However, the research [40] that achieved 97.42% did not consider the entire train and test sets which makes our outcomes better as ignoring some samples while training and testing may affect the performance positively. Moreover, we provided the results for some popular CNN architectures i.e., GhostNet, InceptionV4, Xception, MobileNetV1, MobileNetV2, MobileNetV3 Small, and MobileNetV3 Large architectures in Table 8. Despite being a smaller architecture of 6.5 million parameters, GhostNet did not achieve the desired performance for the considered dataset. InceptionV4 with 43 million parameters and Xception with 23 million parameters achieved a high performance but could not outperform the performance achieved by the proposed BengaliNet architecture. On the other hand, the MobileNet architectures achieved decent performance but could not outperform BengaliNet as well. Additionally, Table 9 illustrates a comparison with some previous studies that focused on the recognition of Bengali handwritten characters by using transfer learning.