## 6 Conclusions  
In modern days, applications of handwritten character recognition (HRC) systems are flourishing. In this paper, to address HCR systems with multiclass classification, a CNN-based model is proposed that achieved exceptionally good results with this multiclass classification. The CNN models were trained with the MNIST digit dataset, which is shaped with 60,000 training and 10,000 testing images. They were also trained with the substantially larger Kaggle alphabet dataset, which comprises over 297,000 training images and a test set which is shaped on testing over 74,490 images. For the Kaggle dataset, the overall accuracies using the 'ADAM' optimizer were 99.516%, 99.511%, and 99.563% for learning rate (LR) 0.001, LR 0.0001, and LR 0.00001, respectively. Meanwhile, the same model using 'RMSprop' achieved accuracies of 99.29%, 99.108%, and 99.191%, respectively, by LR 0.001, LR 0.0001, and LR 0.00001. For the MNIST dataset, the overall accuracies using 'RMSprop' were 99.642%, 99.452%, and 98.142% for LR 0.001, LR 0.0001, and LR 0.00001, respectively. Meanwhile, the same model using the 'ADAM' optimizer achieved accuracies of 99.571%, 99.309%, and 98.142% with LR 0.001, LR 0.0001, and LR 0.00001, respectively. It can be easily understood that, for alphabet recognition, accuracy decreases with the increase in learning rate (LR); contrarily, overall accuracy is proportionately related to LR for digit recognition. In addition, precision, recall, specificity, and \(F1\) score were measured from confusion matrices. Of all the discussed twelve models, the model using the 'ADAM' optimizer with LR 0.00001 obtained a recall value of 99.56%, and the model with LR 0.001 with the 'RMSprop' optimizer obtained the recall value of 99.64%; therefore, these two models excel other models for the Kaggle and MNIST datasets, respectively. As the distribution of the datasets is imbalanced, only the accuracy would be ineffective in evaluating the models; therefore, classification reports (CR) indicating the \(F1\) score for every 10 classes for digits (0-9) and every 26 classes for alphabet (A-Z) were included for the predictions of the best two proposed models. From the CR, we achieved micro, macro, and weighted \(F1\) scores of 0.996 and 0.995, 0.998 and 0.992, and 0.997 and 0.996 for the MNIST and Kaggle datasets, respectively. Furthermore, the obtained results of best two models presented here were compared with the results of other noticeable works in this arena. Considering future work, we intend to include several feature extraction methods by applying a similar framework to that proposed here to more complex languages, such as Korean, Chinese, Finnish, and Japanese.  
Conceptualization, N.S., K.F.H. and A.A.; methodology, N.S. and K.F.H.; software, N.S. and K.F.H.; validation, N.S. and A.A.; formal analysis, N.S.; investigation, N.S. and K.F.H.; writing--original draft preparation, N.S. and K.F.H.; writing--review and editing, N.S., K.F.H., V.P.Y. and A.A.; visualization, N.S. and V.P.Y.; supervision, V.P.Y. and A.A.; project administration, A.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.