## 2 Literature Review  
Previously, various studies have been conducted for the accurate recognition of the handwritten characters of some popular languages, for example, English handwritten characters [10; 11], Chinese handwritten characters [12; 13], Spanish handwritten characters [14; 15], Hindi handwritten characters [16; 17] and so on. However, not a significant amount of studies have been conducted on the Bengali handwritten characters. Moreover, very few contributions are notable in the recognition process of Bengali compound characters. Similar and popular non-Latin languages such as Arabic [18], Chinese [12; 13],Farsi [19], Greek [20], Hindi [16; 17], Japanese [21], Korean [22], Nepali [23], Russian [24], Thai [25], Urdu [26], etc. have received contributions in the domain of handwritten character recognition. Compound characters are the common property of Indian scripts among which Devanagari and Bengali have a huge set of compound characters [27]. Sometimes the shape of the compound character is so complex that it becomes difficult to identify the constituent consonants [27]. Languages that use the Devanagari script such as Hindi [27] suppress the vowel most of the time to bypass the use of compound characters [28] and as a result, few significant works have been conducted regarding Devanagari handwritten compound character recognition previously. However, compound characters are frequently used in Bengali scripts. Therefore, it is important to design a model that can recognize this huge number of compound characters which are highly stylized and morphologically complex. In this section, we have mentioned only the noteworthy studies which paved away the path of the recognition process. Researchers started to implement Bengali basic character recognition long before. Early research on Bengali 50 basic character recognition suggested a Multilayer Perceptron (MLP) technique on stroke features and achieved the highest overall accuracy of 84.33% [29]. The number of samples used for training and testing was 350 and 90 for each of the 50 classes. In another research, the authors applied a Multilayer Perceptron (MLP) classifier on the 76 extracted features (36 longest-run features, 16 centroid features, and 24 shadow features) and achieved an overall accuracy of 75.05% [30]. A total of 10,000 images of size 64 \(\times\) 64 of 50 classes were used in this research and the train-test ratio was 80:20. Later, the study of chain code histogram features suggested an overall accuracy of 92.14% by applying a Multilayer Perceptron (MLP) classifier [31]. In this research, the authors used 200 samples for each of the 50 classes for training and 10,187 samples for testing where each of the test classes had at least 60 samples per class. The images were scanned at 300 dpi resolution. After that, CMATERdb datasets were introduced [32; 33] which not only contained Bengali basic characters but also contained compound characters, numerals, and modifiers. Since then, a notable number of studies have been conducted on different CMATERdb datasets. One research suggested a deep convolutional neural network with a learning rate of 0.001 and the train-validation ratio of 80:20 for the recognition of 50 basic Bengali handwritten characters [34]. Later, MobileNetV1 architecture was used for recognizing the basic Bengali handwritten characters [35]. [PERSON] et al. applied a Hybrid-HOG-based Convolutional Neural Network approach and achieved 92.57% and 92.77% accuracy for 171 and 199 Bengali compound characters respectively [36]. [PERSON] et al. suggested a deep convolutional network-based approach with ELU and dropout to achieve an overall accuracy of 93.68% although they considered 8,000 test images and 34,000 train images [37]. Although a significant amount of research has not been conducted on Bengali basic and compound characters, a significant amount of works has been conducted on the Bengali numerals recognition.