## 3 Our Contributions  
In this research, we have proposed a low-cost novel deep convolutional neural network architecture, BengaliNet, for Bengali handwritten character recognition. The architecture uses 2.24 million to 2.43 million parameters only, based on the number of output classes (7, 10, 50, 171, 199, and 256). The reason for focusing on a fewer number of parameters is that Bengali handwritten character recognition is supposed to be an application for mobile devices. Hence, low-cost models are suitable for such applications. The existing architectures in previous research used more parameters than ours. For example, VGG-16, Alex Net, and ResNet-50 have been implemented in previous research and used almost 138 million, 61 million, and 23 million parameters [44; 45]. MobileNetV1 architecture uses the least number of parameters that is a total of 4.2 million parameters, but this architecture traded off low-cost implementation with accuracy, hence, produced a lower accuracy [35].  
However, the latest work in the domain of Bengali handwritten character recognition used 4.75 million parameters for training by not trading off the overall accuracy [40]. This work outperformed all previous studies and the authors compared their architecture with AlexNet, VGGNet, and GoogLeNet as well. They used \(64\times 64\) size input images for their research. In our research, we used a novel deep convolutional neural network with an input size of \(28\times 28\). As the main focus of this research is to design a low-cost high-performance network for mobile devices, the input size was kept as small as possible to minimize the overall number of parameters without sacrificing the performance. We not only used only 2.24 to 2.43 million parameters (almost half parameters of the latest research) but also outperformed the latest work by a fine margin as well. Details on our architecture can be found in the \"Materials and Methods\" section and comments on why our proposed architecture is novel and works better can be discovered in the \"Experimental Analysis\" section.  
Further inspection of misclassifications revealed that almost all the misclassifications were occurring due to some common issues. Some of the classes had highly similar samples, some of the classes had multiple writing fashions and some of the classes had some noisy samples that are even difficult to recognize by human eyes. With proper examples and figures, misclassifications have been discussed in the \"Experimental Analysis\" section. We believe commenting on misclassifications will further showcase the efficiency of our proposed architecture and significantly justify our contributions. To measure the efficiency and impact of the proposed architecture in developing Bengali handwritten character recognition tools, other Bengali datasets of different distributions were used for testing i.e., Ekush, BanglaLekha, and NumtaDB. These datasets were not used while training, hence, the samples are unknown to the classifier. Despite coming from different distributions, our architecture achieved an overall accuracy of 98.36%, 99.13%, 96.09%, 98.11%, and 97.50% for Ekush basic and numerals, BanglaLekha basic and numerals, and NumtaDB numerals respectively. Thus, we believe our contributions will be significant in the domain of Bengali handwritten character recognition.