## 5 Results and Analysis
For simplification, let us also assume that each of the 26 letters in the alphabet (A-Z) has 100 images for each of the letters, totaling 2600 images altogether. If we assume that the model accurately identifies the images of the letter 'A' in 97 out of 100 images, then we say that the accuracy of the model is 97%. Thus, we can also conclude that the total number of true positives (TTPs) is 97. Under the same assumptions as above, if the letter 'O' is incorrectly identified as 'A', then this would tell us that the number of total false positives (TFPs) in this case would be 1. If the letter 'A' has been misidentified as 'O' three times in the model, then the total number of false negatives (TFNs) for this model is 3. The rest of the 2499 images of the 2600 images are then considered as the total true negative (TTN). Figures 12 and 13 show the confusion matrices for the best two models (model K5 for letter recognition and model M2 for digit recognition) established in terms of overall performance that were trained and validated with the Kaggle and MNIST datasets, respectively. Figure 8: Validation accuracy of the six models for English alphabet recognition. (**a**) Optimizer—’ADAM’s learning rate—0.001. (**b**) Optimizer—’ADAM’; learning rate—0.0001. (**c**) Optimizer—’ADAM’; learning rate—0.00001. (**d**) Optimizer—’RMSprop’; learning rate—0.001. (**e**) Optimizer—’RMSprop’; learning rate—0.0001. (**f**) Optimizer—’RMSprop’; learning rate—0.00001. Figure 9: Validation accuracy of the six models for digit (0–9) recognition. (**a**) Optimizer—’ADAM’s learning rate—0.001. (**b**) Optimizer—’ADAM’; learning rate—0.0001. (**c**) Optimizer—’ADAM’s learning rate—0.0001. (**d**) Optimizer—’RMSprop’; learning rate—0.001. (**e**) Optimizer—’RMSprop’; learning rate—0.0001. (**f**) Optimizer—’RMSprop’; learning rate—0.0001. Figure 10: Validation loss of the six models for English alphabet recognition. (**a**) Optimizer—ADAM’2, learning rate—0.001. (**b**) Optimizer—ADAM’; learning rate—0.0001. (**c**) Optimizer—ADAM’; learning rate—0.0001. (**d**) Optimizer—RMSprop’; learning rate—0.001. (**e**) Optimizer—RMSprop’; learning rate—0.0001. (**f**) Optimizer—RMSprop’; learning rate—0.0001. Figure 11: Validation loss of the proposed six models for digit (0–9) recognition. (**a**) Optimizer—’ADAM’s; learning rate—0.001. (**b**) Optimizer—’ADAM’; learning rate—0.0001. (**c**) Optimizer—’ADAM’; learning rate—0.00001. (**d**) Optimizer—’RMSprop’; learning rate—0.001. (**e**) Optimizer—’RMSprop’; learning rate—0.0001. (**f**) Optimizer—’RMSprop’; learning rate—0.00001. Figure 12: Confusion matrix of model K5 for A–Z recognition. Figure 13: Confusion matrix of model M2 for 0–9 recognition. Precision deals with the percentage of the relevant results, whereas accuracy states how close the real values are to the generated values. Sensitivity, identified as recall and true negative rate, known as specificity, are other important factors for investigating a CNN model. The \(F1\) score is the weighted average of the combination of both precision and recall. Equations (1)-(5) represent accuracy, specificity, recall, precision, and \(F1\) score, respectively. \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{2}\]  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
Figure 8:0  
\[F1\ Score=2*\left( \begin{array}{c}Precision*Recall\\ \hline Precision+Recall\end{array} \right) \tag{5}\]  
With the Kaggle dataset of 74,490 testing images for letter recognition, using 'ADAM' optimizer model K1 detects 74,130 TTP and 360 TFP images, model K3 finds 74,126 TTP and 364 TFP images, whereas model K5 detects 74,165 TTP and 325 TFP images. Then, again, the models using 'RMSprop' underperform in identifying the TFP cases, which is above 500 for each model, while the TTP cases detected using 'RMSprop' are 73,963, 73,826, and 73,888 by models K2, K4, and K6, respectively. The recall of model K5 is 99.56%, performing better than the other investigated models.