## 6 Conclusions and Future Scopes  
Despite being the seventh most-spoken language in the world, Bengali has not received many contributions in this domain due to a huge number of characters with complex shapes. Previously, very few studies on Bengali handwritten character recognition addressed the importance of low-cost architecture. However, they traded off the performance with fewer parameters. In this study, the focus was on designing a low-cost novel convolutional neural architecture for the Bengali handwritten character recognition so that the recognition can be done on mobile devices. The proposed model called BengaliNet uses only 2.24 to 2.43 million parameters based on the number of output classes and takes an input of size \(28\times 28\) only. When designing the architecture, branches, and filters of multiple sizes i.e., \(3\times 3\), \(5\times 5\), and \(7\times 7\) were used which extracted better deep features. Finally, the SoftMax averaging technique was used to make the final decision for the character recognition. The experimental results showed that the proposed architecture can achieve better accuracy than the baseline approaches and the methods used in previous research in terms of performance and number of parameters. The results also showed that the proposed model obtains similar performance for previously unknown datasets as well. We believe the contributions of our study will be beneficial for future research such as automatic digitization of historical documents, automated license plate recognition, automated postal service, and so on. In the future, we will focus on improving the architecture more and apply the architecture to more datasets. Furthermore, the lack of an ideal Bengali handwritten character dataset is also a dilemma in this domain of research. In the future, we will work in this regard also.  
The following are available online at [[https://www.mdpi.com/2076-3417/11/15/6845/s1](https://www.mdpi.com/2076-3417/11/15/6845/s1)]([https://www.mdpi.com/2076-3417/11/15/6845/s1](https://www.mdpi.com/2076-3417/11/15/6845/s1)).  
Conceptualization, A.S.; methodology, A.S. and M.A.M.H.; software, A.Y.S. and M.M.H.; validation, M.A.M.H. and J.S.; formal analysis, A.Y.S. and M.M.H.; investigation, M.A.M.H. and J.S.; resources, A.S.; data curation and collection, A.Y.S. and M.M.H.; writing--original draft preparation, A.S.; writing--review and editing, A.Y.S. and M.M.H.; visualization, A.Y.S. and M.M.H.; supervision, M.A.M.H.; project administration, J.S.; funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All the datasets used in this research are publicly accessible. CMA-TERdb Datasets are available at: [[https://code.google.com/archive/p/cmaterdb](https://code.google.com/archive/p/cmaterdb)]([https://code.google.com/archive/p/cmaterdb](https://code.google.com/archive/p/cmaterdb)), accessed on 16 May 2021. Ekush dataset can be accessed at [[https://shahariarrabby.github.io/ekush](https://shahariarrabby.github.io/ekush)]([https://shahariarrabby.github.io/ekush](https://shahariarrabby.github.io/ekush)), accessed on 16 May 2021, BanglaLekha dataset can be found at [[https://data.mendeley.com/datasets/hfgsf8](https://data.mendeley.com/datasets/hfgsf8) zrkc/2]([https://data.mendeley.com/datasets/hfgsf8](https://data.mendeley.com/datasets/hfgsf8) zrkc/2), accessed on 16 May 2021, and NumtaDB dataset is available at [[https://www.kaggle.com/BengaliAI/numta](https://www.kaggle.com/BengaliAI/numta)]([https://www.kaggle.com/BengaliAI/numta](https://www.kaggle.com/BengaliAI/numta)), accessed on 16 May 2021.  
The authors declare no conflict of interest.  
Figure 12: Examples for characters with multiple writing fashions.