## 3 Proposed Methodology
### Classification  
A convolutional neural network (CNN) is a neural network architecture specially developed to process input images. It is popularly used in various types of problems such as image and video recognition and image classification; it is also used for text-related and time series problems and other applications [35; 36; 37; 38; 39]. A CNN is built by stacking three kinds of layers: convolutional layers, pooling layers, and fully connected layers. To these main layers, the dropout layer and the activation function are added. The convolutional layer has a prime role in this architecture as this layer extracts features from the input. It contains convolutional kernels represented by a neuron which works by splitting the images into smaller blocks for the extraction of feature patterns. The multiplication of the input image by a filter of a certain size is performed by the kernels' interaction with the images based on weights. The operation can be portrayed through Equation (16), where \(j_{d}(p,q)\) is an occurrence of the input vector \(j_{d}\) multiplied by the \(i_{c}^{j}(k,l)\) index of the \(j\)th kernel of the \(c\)th layer.  
\[f_{c}^{k}(m,n)=\sum_{d}\sum_{p,q}j_{d}(p,q).i_{c}^{j}(k,l) \tag{16}\]  
The output mapping of the \(j\)th kernel is obtained using Equation (17)  
\[F_{c}^{j}=[f_{c}^{j}(1,1),\dots,f_{c}^{j}(m,n),\dots,f_{c}^{j}(M,N)] \tag{17}\]  
The pooling layer follows the convolutional layer. It decreases the size of the feature map, which reduces computational costs. This operation is done by reducing the relations between the layers and operates independently on each feature map as shown by Equa  
Figure 8: The feature visualizations of one document by four authors using pie charts. Each area represents each feature value obtained for 17 features used in the experiment. Here, the “hsv” theme was used for distinguishing the feature values from one another.  
tion (18), where \(Y_{c}^{j}\) determines the pooled feature map of the \(c\)th layer for the \(j\)th kernel and \(O_{p}\) denotes the pooling operation.  
\[Y_{c}^{j}=O_{p}(F_{c}^{j}) \tag{18}\]  
In this paper, a network was proposed where the inputs were fed to a 32-dimensional convolution layer. The present experiment for training the convolutional neural network model depended on the labeled instances extracted from the handcrafted features for supervised learning [40; 41]. The output from this layer was not pooled to avoid a loss of clarity and was passed on to a second convolution layer with 16 dimensions. The dimensions were chosen based on experimental trials. The filter sizes of the 1 st and 2 nd convolution layers were set to 5 and 3, respectively, after experimental trials. The output of this second layer was max pooled with a window size of 3 followed by a dropout layer, whose result was passed to two dense layers with 256 and 100 dimensions, respectively. The output of the final dense layer was passed on to the fully connected layer. The network was initially trained with images of \(100\times 100\) pixels and a batch size of 100 instances for 100 epochs. The dropout was set at 50%. The convolution and the first two dense layers had a ReLU activation function, while the final dense layer had a softmax activation function. The architecture of the proposed network is illustrated in Figure 9.  
The total number of parameters for each layer are summarized in Table 4.