worked on deep neural networks (DNN) for MNIST and obtained a 0.19% error rate [17]. Nevertheless, after a vigilant investigation, it has been observed that the maximal recognition accuracy of the MNIST dataset can be attained by using only ensemble methods, as these aid in improving the classification accuracy. However, there are tradeoffs, i.e., high computational cost and increased testing complexity [18]. In this paper, a tailored CNN model is proposed which attains higher accuracy with light computational complexity. Research on HCR technology has been going on for long time now and it is in use by the industry, but the accuracy is low, which compromises the usability and overall performance of the technology. Until now, the character recognition technologies in use are still not very dependable and need more development to be deployed broadly for unfailing applications. On this account, characters of the English alphabet and digit recognition are performed in this paper by proposing a custom-tailored CNN model with two different datasets of handwritten images, i.e., Kaggle and MNIST, respectively, which achieve higher accuracies. The important features of these proposed projects are as follows:  
1. In the proposed CNN model, four 2D convolutional layers are kept the same and unchanged to obtain the maximum comparable recognition accuracy into two different datasets, Kaggle and MNIST, for handwritten letters and digits, respectively. This proves the versatility of our proposed model. 2. A custom-tailored, lightweight, high-accuracy CNN model (with four convolutional layers, three max-pooling layers, and two dense layers) is proposed by keeping in mind that it should not overfit. Thus, the computational complexity of our model is reduced. 3. Two different optimizers are used for each of the datasets, and three different learning rates (LRs) are used for each of the optimizers to evaluate the best models of the twelve models designed. This suitable selection will assist the research community in obtaining a deeper understanding of HCR. 4. To the best of the authors' knowledge, the novelty of this work is that no researchers to date have worked with the classification report in such detail with a tailored CNN model generalized for both handwritten English alphabet and digit recognition. Moreover, the proposed CNN model gives above 99% recognition accuracy both in compact MNIST digit datasets and in extensive Kaggle datasets for alphabets. 5. The distribution of the dataset is imbalanced. Hence, only the accuracy would be ineffectual in evaluating model performance, so advanced performances are analyzedto a great extent with a classification report for the best two proposed models for the Kaggle and MNIST datasets, respectively. Classification reports indicate the \(F1\) score for each of the 10 classes for digits (0-9) and each of the 26 classes for alphabet (A-Z). In our case of multiclass classification, we examined averaging methods for the \(F1\) score, resulting in different average scores, i.e., micro, macro, and weighted average, which is another novelty of this proposed project. The rest of the paper is organized as follows: Section 2 describes the review of the literature and related works in the handwritten character recognition research arena; Sections 3 and 4 present datasets and proposed CNN model architecture, respectively; Section 5 discusses the result analysis and provides a comparative analysis; and Section 6 describes the conclusion and suggestions for future directions.