## 1 Introduction  
Optical Character Recognition (OCR) is the mechanical or electrical conversion of pictures of handwritten or printed typed text into machine-encoded texts from either a scanned document or an image of the document [1]. It is widely practiced to recognize the texts in number plates of vehicles, passports, historical documents, business cards, printed materials, bank checks, etc. In this research, we focused on handwritten character recognition as it is more complex to recognize handwritten characters with various writing fashions than typed characters with one specific writing fashion. The main barrier to handwritten character recognition is the variation of literature worldwide. As of 2020, there are nearly 7117 languages in the world [2]. Each of these languages has its own alphabet or letters. Hence, language-specific character recognition has been an area of interest for a long time now. Beforehand, while some of the languages have received many contributions in the area of handwritten character recognition, others did not receive any significant contributions. In this study, we focused on Bengali handwritten character recognition,The reason for choosing Bengali literature and the challenges of character recognition for the Bengali language have been presented in the following two paragraphs. Bengali, an Indo-Aryan language, is the seventh most-spoken language worldwide and fifth most-spoken native language globally with nearly 228 million native speakers and approximately 37 million second-language speakers [3; 4; 5]. Bengali is the most widely spoken language in Bangladesh, with 98% of Bangladesh being fluent in Bengali as their primary language and for this reason, Modern Standard Bengali is the national language of Bangladesh [6]. Moreover, Bengali is the second most-spoken language among the 22 scheduled languages of India just behind Hindi and the official language of West Bengal, Tripura, and the Barak Valley of Assam states of India. Moreover, it is the most-spoken language in the Andaman and Nicobar Islands located at the Bay of Bengal [7] and is practiced by notable speakers in Uttarakhand, Nagaland, Mizoram, Meghalaya, Jharkhand, Chhattisgarh, Delhi, and Arunachal Pradesh states of India [7]. Bengali is the only language in the world for which people sacrificed their lives on 21 February 1952 and after the independence of Bangladesh in 1971, UNESCO recognized 21 February as the International Mother Language Day in 1999 [8]. As it is one of the most-spoken, popular, and precious languages of the world, Bengali handwritten character recognition has been an area of interest for researchers for decades now. The phenomenon which turned Bengali handwritten character recognition into a complex problem is none other than the number of characters in Bengali literature. Unlike other popular languages, Bengali has not only 50 basic characters but also has many compound characters which makes the total number of characters in Bengali literature approximately 400 [9]. Although the recent development in the domains of Transfer Learning and Deep Learning, 1000 classes can be taken care of by using complex neural networks, still Bengali character recognition is a tough task as Bengali characters are more complex and cursive in shape, especially the compound characters. Moreover, because it has a huge number of characters, some of the characters are extremely similar and some characters have multiple writing fashions which makes the recognition process more complex. Therefore, it is a profound domain of interest. In this research, first, we chose publicly available and one of the most popular benchmark datasets, the CMATERdb Bengali handwritten character dataset for the training phase which contains 50 basic characters, 7 modifiers, 171 unique compound characters with 199 different writing styles, and 10 numerals of Bengali literature.