## 4 Proposed Convolutional Neural Network  
Of all the deep learning models in image classifications, CNN has become very popular due to its high performance in recognizing image patterns. This has opened up various application opportunities in our daily life and industries which include medical image classification, traffic monitoring, autonomous object recognition, facial recognition, and much more. CNNs are sparse, feed-forward neural networks [61]. The idea of an artificial neuron was first conceptualized in 1943. [PERSON] and [PERSON] first found that, for detecting lights in the receptive fields, visual cortex cells have a major role, which greatly inspired building models such as neocognitron. This model is considered to be the base and predecessor of CNN. CNN is formed of artificial neurons which have a self-optimization property, learning like brain neurons. Due to this self-optimizing property, it can extract and classify the features extracted from images more precisely than any other algorithm. Moreover, it needs very limited preprocessing of the input data, while yielding highly accurate and precise results. CNNs are vastly used in object detection and image classification, including medical imaging. In image classification, each pixel is considered a feature for the neural network. CNN tries to understand and differentiate among the images depending  
Figure 4: Total distribution of Kaggle letters (A–Z). on these features. Conventionally, first few convolutional layers capture very low-level features, such as the edges, gradient orientation, or color. However, with the increased number of convolutional layers, it starts extracting high-level features. Due to the higher dimensionality and convolution, the parameters of the network increase exponentially. This makes the CNN computationally heavy. However, with the development of computational technology and GPU, these jobs have become much more efficient. Moreover, the development of the CNN algorithms has also prompted the ability to reduce dimensionality by considering small patches at a time which reduces the computational burden without losing the important features. Handwritten character recognition (HCR) with deep learning and CNN was one of the earliest endeavors of researchers in the field. However, with increased modeling efficacy and the availability of a huge dataset, current models can perform significantly better than the models of ten years ago. However, one of the challenges of the current models is generalization. The model that performs excellently with one dataset may perform poorly with a different one. Thus, it is important to develop a robust model which can perform with the same level of accuracy across different datasets, which would give the model versatility. Thus, a CNN model is designed which is computationally proficient because of its optimized number of CNN layers, while performing with high accuracy across multisource massive datasets. Owing to the lower resolution of the handwritten character images, the images which were fed to the input layers were sized 28 \(\times\) 28 pixels. The input layer feeds the images to the convolutional layers, where the features are convolved. The model has only four convolutional layers, which makes it lightweight and computationally efficient. The first layer is a 2D convolutional layer with a 3 \(\times\) 3 kernel size and rectified linear unit (ReLU)-activation function. ReLU is one of the most widely used activation functions in deep learning algorithms. ReLU is computationally effective because the neurons are not activated altogether like the other activation functions, e.g., tanh [62]. ReLU is a piecewise linear function which is also continuous and differentiable at all points except for 0. Besides providing simplicity and empirical simplicity, it also has reduced likelihood of vanishing gradient. Because of the abovementioned benefits, and as per the suggestion of the literature that ReLUs tend to converge early, it was chosen for our model.