###### Abstract
Hence, designing a handwriting recognition system considering all sorts of handwriting styles with a high range of accuracy is challenging. Several approaches have been used for this purpose like Structural Methods, Convolutional Neural Networks, Syntactic Methods, Back Propagation, K-Nearest Neighbor Algorithm, Support Vector Machine (SVM), etc. Most of the existing works in this area are image-based which focused on image pre-processing techniques and so on. In an image-based system, the quality of images depends on lights, effects, reflection, image quantization, brightness, camera attributes, camera frames, and image quality. Hence, the system accuracy is affected by the image quality, contrast, lighting, shading, noise suppression, filtering, etc. Depending on image pre-processing, the time complexity and computational processing cost are also high for the existing systems. Moreover, an image-based model does not work with the genuine and exact numerical values of an individual's handwriting data. Direct numerical values of a person's handwriting data provide more genuine and accurate features of an individual user which is crucial for the authentication of a writer. Therefore, in this research, we concentrate on collecting the more genuine numerical values of the user's handwriting directly from the sensors signal of digital pen-tablet devices which motivates this work. The process of getting direct data values helps the proposed method to get more improve accuracy in the context of pen-tablet sensor data. Hence, in this research, we emphasized more robust and efficient data acquisition and analysis which is invariant of the camera frame, camera positioning, image quality, and image pre-processing techniques. The proposed model is constructed based on the user's pen tablet handwriting data analysis which automatically stores the numeric values of the user's handwriting attributes from the pen-tablet sensor signals. In the proposed system, since the data values are directly extracted from the sensor signal of digital pen and tablet devices, it ensures the robustness of the identification of handwriting data. In this research, we emphasize real-time handwritten sensor data analysis of individuals by using pen tablet devices. In the proposed user authentication model, firstly, six completely separate parameters of handwritten data are collected from the sensor signals of digital pen-tablet devices. Before feature extraction, data pre-processing has been done. In the feature extraction phase, several discriminant kinematic and statistical features are extracted from the parameters of handwriting data. However, all the features extracted from high dimensional sensor data are not equally important to identify a user. Hence, to keep our trained model focused only on the vital features, we introduce the optimal feature selection model which minimizes the overfitting problem. Then, the most informative and vital features are selected using the optimal feature selection model. Finally, machine learning techniques are applied for training and testing the selected optimal features for the user authentication process. The proposed system is implemented using different classification algorithms and the experimental analysis shows that we got satisfactory results which ensure the practicality of our system with low implementation cost. Our main contributions in this research work are:  
User authentication using motion sensor data of pen-tablet devices. A quantitative analysis of pen-tablet sensor data using kinematic and statistical features extraction model. Introduce optimal feature selection model combination of filter-based approach and wrapper approach. An efficient and robust writer identification model using support vector machine (SVM), logistic regression (LR), and random forest (RF) classifier. The full paper is organized as follows: the next section provides the literature review.