## 2 Review of Literature and Related Works  
Many new techniques have been introduced in research papers to classify handwritten characters and numerals or digits. Shallow networks have already shown promising results for handwriting recognition [19; 20; 21; 22; 23; 24; 25; 26]. [PERSON] et al. investigated deep belief networks (DBN), which have three layers along with a grasping algorithm, and recorded an accuracy of 98.75% for the MNIST dataset [27]. [PERSON] et al. improved the performance of recurrent neural networks (RNNs), reducing the word error rate (WER) and character error rate (CER) by employing a regularization method of dropout to recognize unconstrained handwriting [28]. The convolutional neural network (CNN) delivered a vast change as it delivers a state-of-the-art performance in HCR accuracy [29; 30; 31; 32; 33]. In 2003, for visual document analysis, a common CNN architecture was introduced by [PERSON] et al., which loosened the training of complex methods of neural networks [34]. [PERSON] et al. used multilayer CNNs for end-to-end text recognition on benchmark datasets, e.g., street view text and ICDAR 2003, and accomplished brilliant results [35]. Recently, for scene text recognition, [PERSON] et al. introduced a new approach, the conventional recurrent neural network (CRNN), integrating both the deep CNN (DCNN) and recurrent neural network (RNN), and announced its superiority to traditional methods of character recognition [36]. For semantic segmentation, [PERSON] et al. proposed a deep convolutional network architecture where the max-pooling layer was used to obtain good performance; the authors also compared their model with current techniques. The segmentation architecture known as SegNet consists of a pixel-wise classification layer, an encoder network, and a decoder network [37; 38]. In offline handwritten character recognition, CNN has shown outstanding performance for different regional and international languages. Researchers have conducted studies on Chinese handwritten text recognition [39; 40; 41]; Arabic language [42]; handwritten Urdu text recognition [43; 44]; handwritten Tamil character recognition [45]; Telugu character recognition [46]; and handwritten character recognition on Indic scripts [47]. [PERSON] et al. used features extracted from a CNN in their model and recognized the informative local regions in [48] from recent character images, accomplishing a recognition accuracy of 95.96% by applying a novel multi-objective optimization framework for HCR which comprises handwritten Bangla numerals, handwritten Devanagari characters, and handwritten English numerals. High performance of the CROHME dataset was observed in the work of [PERSON] et al. [49]. The author employed a multiscale CNN for clustering handwritten mathematical expression (HME) and concluded by identifying that their model can be improved by training the CNN with a combination of global, attentive, and max-pooling layers. Recognition of word location in historical books, for example on [PERSON]'s Bible pages, is wisely addressed in the work of [PERSON] et al. [50] by developing an R-CNN-based deep learning framework. [PERSON] et al. introduced an intelligent character recognition (ICR) system, logically using a conventional neural network [51]. IAM datasets and French-language-based RIMES lexicon datasets were used to evaluate the model,which reported a commendable result. The variance between model parameters and hyper-parameters was highlighted in [52]. The hyper-parameters include the number of epochs, hidden units, hidden layers, learning rate (LR), kernel size, activation function, etc., which must be determined before the training begins to determine the performance of the CNN [53]. It is mentioned that, if the hyper-parameters are chosen poorly, it can lead to a bad CNN performance. The total number of hyper-parameters of some CNN models are 27, 57, 78, and 150, respectively, for AlexNet [54], VGG-16 [55], GoogleNet [56], and ResNet-52 [57].