## 5 Experimental Analysis
### Why Proposed Architecture Performs Better? BengaliNet works better mainly because of its three-way architecture. When designing the BengaliNet architecture, different combinations of filter sizes were taken into consideration. It was observed that many Bengali characters have complex shapes. To extract these complex shapes, multiple convolutions were needed but to design a deep network, the input size needs to be larger too. These type of architectures already exists i.e., AlexNet, VGG-16, ResNet-50, etc. But the main problem of these architectures is that these architectures are heavy and have 23-138 million parameters. Bengali handwritten character recognition is supposed to be conducted in mobile devices, hence, a low-cost architecture was needed. \(3\times 3\), \(5\times 5\), or \(7\times 7\) individual architectures were able to produce a decent accuracy as even in these individual architectures, there were branches. Convolutions with batch normalization and normal convolutions ensured better learning than sequential models. When designing BengaliNet architecture, we took filters of multiple sizes under consideration. The filter size of the convolution layers in three pathways were \(3\times 3\), \(5\times 5\), and \(7\times 7\) respectively. Moreover, each of these paths has its own branches with multiple convolution layers, batch normalization, and dropouts. Merging features obtained from the general convolution and the convolution with batch normalization ensured better learning than a simple sequential model. It was observed that \(5\times 5\) and \(7\times 7\) architectures were being able to learn some complex features which were not being learned by \(3\times 3\) architecture. Hence, these three architectures were combined. Instead of choosing the voting method, the SoftMax averaging technique was chosen for this research. As there were only three architectures in consideration, the voting method was depending on random class selection when the three architectures were producing different test outcomes. But for the SoftMax averaging technique, this randomness can be reduced. Suppose, for a specific test sample, the three paths were predicting A, B, and C classes. In such cases, the voting method chooses a random class among three different predicted classes which can either produce a better accuracy or a worse accuracy. On the other hand, the SoftMax averaging technique takes the average of three SoftMax arrays produced by three paths and therefore, there is a high possibility to omit the possible randomness of the voting method. In Table 10, the overall accuracy for the voting method and the SoftMax averaging method have been presented. It can be noticed that the SoftMax averaging technique is producing better performance. When there are many architectures under consideration, the efficiency of the voting method will increase. But as in this research, the focus was specifically on cost-effectiveness so that recognition can be performed in mobile devices, only three small architectures were considered. Therefore, with evidence of Table 10, we chose the SoftMax averaging technique. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods and Datasets** & **Criteria** & **Previous Works** & **BengaliNet** \\ \hline \multirow{2}{*}{VGG-16 on 3.1.1 [44]} & Parameters & 138 M & **2.24 M** \\  & Accuracy & 93.33\% & **99.01\%** \\ \hline \multirow{2}{*}{AlexNet on 3.1.1 [44]} & Parameters & 61 M & **2.24 M** \\  & Accuracy & 95.00\% & **99.01\%** \\ \hline \multirow{2}{*}{ResNet-50 on  BanglaLekha [45]} & Parameters & 23 M & **2.24 M** \\  & Accuracy & 96.12\% & **98.11\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison with previous studies in terms of total parameters and overall accuracy. Because of combining three lightweight architectures, excessive learning by extracting deep features using big or transfer learned architectures could be ignored.