## 3 Proposed Model  
In this section, the proposed system model of writer identification is explained in a step-by-step procedure. The proposed system consists of six phases: data acquisition, data analysis and prepossessing, feature extraction, optimal feature selection, and classification. In the data collection phase, handwriting data is collected from the sensor signal of a digital pen tablet device which generates six attributes (Time, Pressure, \(X\)-axis, \(Y\)-axis, Horizontal angle, and Vertical angle) which represents different unique numerical values for different types of handwriting. To make a balanced format of dataset some functions such as Imputation Function and Flatten Function are utilized. Our handwriting dataset consists of real-time handwritten data and every data is important. After data analysis, it is observed that some data values are missing and some became very high which can be a problem for our machine learning model. Using a direct mean of the data may cause the loss of some attributes of the data such as pressure, angle, etc. Since, missing values of the handwritten data are not ignorable; data imputation has the highest potential to preserves the mean of the non-missing data. In our research, we have utilized mean imputation to replace the missing data values. On the other hand, Flatten function is utilized to convert the 2D data into a one-dimensional array for passing it to the next layer. We flatten the dataset to keep the level same for all the data. It makes our computational process simple and easier. Hence, to enhance our dataset and to make it normalize, we have utilized some pre-processing techniques such as imputation function and flatten function. These pre-processing makes our dataset balanced (ready) for the implementation and experiment. That is why we called it as a balanced format of dataset.  
After data pre-processing, different statistical and kinematic features are extracted. The feature selection model is introduced to find the optimal features which are vital for the identification of a user. Three different and most widely used machine learning algorithms are used for classification which are: Support Vector Machine (SVM), Linear Regression (LR), and Random Forest (RF) classifiers.  
A general block diagram of the user identification model is shown in Figure 1. The model is divided into two major parts. In this research, the first part represents our main contribution to the experimental analysis. The first part consists of extracting different statistical and kinematic features from the dataset and the optimal feature selection process. The dataset is split into two subsets. The first subset is used to generate different optimal features using a hybrid feature selection process. Several kinematic and statistical features are calculated from the six parameters (time, pressure, \(x\)-axis, \(y\)-axis, horizontal angle, and vertical angle) of sensor signals from the first data subset. In this research, for better accuracy, we have considered both statistical and kinematic features. The proposed model introduces a hybrid feature selection algorithm to find out the important feature sets. In the second phase, training and testing are completed using the second data subset. Two third of the dataset is used for feature selection and training process and one-third of the dataset is used for the testing process. A machine learning technique is deployed to identify the person's handwriting based on the classified optimal features set. We have used SVM, LR, and RF classifiers to compare the accuracy of the classified features in this research and to show the stability of our proposed model.