## 5 Results and Analysis
In contrast, model K4 attained the lowest recall percentage of 99.1% (which is also above 99%) in comparison with others. From the confusion matrices, it was noted that all the proposed models achieved the specificity of 99% and model K5 performed supremely for recognizing handwritten letters. It is important to recall that, in multiclass classification, we compute the \(F1\) score for each class in a one-versus-the-rest (OvR) tactic, instead of a single overall \(F1\) score, as perceived in binary classification. Therefore, the total true negative (TTN) number is vital to evaluate the proposed model. With the Kaggle dataset, model K5 detected 1,861,925 TTN images, which was the highest number detected; contrarily, model K4 detected the lowest number, at 1,861,586 TTN images. The overall performance and the total number of TTN images detected of the proposed model K5 showed better results than the other models, as it is always expected that higher TTN cases will be obtained for each class while recognizing specific letters. The accuracy of the proposed model K5 was 99.563%, with precision and recall values of 99.5% for both the parameters, which is the highest handwritten alphabet recognition accuracy known to the authors for the Kaggle dataset. Now, for the MNIST dataset for digit recognition, model M1, model M2, and model M3 performed with accuracies of 99.571%, 99.309%, and 98.238%, respectively, whereas model M2, model M4, and model M6 performed with accuracies of 99.642%, 99.452%, and 98.142%, respectively. Model M2 ('RMSprop' with LR 0.001) achieved the highest precision of 99.6, whereas model M6 obtained the lowest precision of 98.1 (which is also above 98 %). Similarly, the highest value of recall was 0.9964 by model M2, and the lowest recall value of 0.9814 was by model M6. It was also seen that, from confusion matrices, with MNIST 5000 test patterns for digit recognition, model M2 had the highest, preeminent performance in comparison with other models, because it found 4185 TTP and 15 TFP images, and had the highest TTN case of 37,785. Model M6 performed poorest when compared with other models; the TTP and TFP cases are 4122 and 78, respectively, and the TTN cases decreased by 65 images compared with model M2; however, while recognizing specific digits, it was expected to always obtain higher TTN cases for each class. Table 2 shows the comparative results with the Kaggle and MNIST datasets of all the models. To classify both the English alphabets and digits, using two different optimizers, four 2D convolutional layers are kept the same and unchanged in the proposed CNN model. The only difference is the number of output classes, defined in the last fully connected layer, which is the 'fully connected + Softmax' layer. The number of classes is 10 and 26 for digit and alphabet recognition, respectively. It is clearly seen from Table 2 that the 'ADAM' optimizer performs better than 'RMSprop' for letter recognition, whereas, for digit recognition, 'RMSprop' is more suitable. These optimizers are used here to obtain fast-tracked results by changing the attributes of the proposed neural networks. For alphabet recognition, with the Kaggle dataset, it shows that the models (i.e., K1-K6) perform better with the decrement of one of the hyper-parameters, the learning rate (LR); contrariwise, with the MNIST dataset, for digit recognition, it displays that the models (model M1-model M6) perform well with the increment of the learning rate, e.g., the overall accuracy increases to 1.53% while using 'RMSprop', and the learning rate increases from 0.00001 to 0.001 for the MNIST dataset. It can be seen from Figures 12 and 13 that the distribution of the dataset is imbalanced. Hence, only the accuracy would be ineffective in judging model performance and so the classification report (CR) is indispensable for an analytical understanding of the model predictions.