applied sciences Article  
Online Kanji Characters Based Writer Identification Using Sequential Forward Floating Selection and Support Vector Machine  
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identification using different language-based characters such as Arabic [4], Bangla [5], Chinese [6,7], French [8], Japanese [8, 9, 10], and English [11, 12, 13, 14, 15].  
In the field of handwriting analysis, the goal is to propose an efficient method that can be compared with existing approaches that use veins, fingerprints, and other biometrics. There are two ways of collecting handwritten samples for writer identification: offline and online character recognition methods. In offline-based identification or static method, writing-based samples are collected from documents or images using scanners. Generally, this method is built on several attributes, including lines, characters, words, and so on [16]. The dynamic features are not present in the offline mode. Moreover, there is a lack of sequential information in handwriting and huge intra-class variation. As a result, offline writer recognition is considered more complex. Research on writer identification based on offline methods is a very challenging issue for security and forensic purposes [7]. As a result, many studies have been conducted using offline methods [8,17,18].  
Online handwritten characters are considered less challenging due to having various information such as pen pressure, pen altitude, pen azimuth, and pen position [7]. Moreover, handwritten characters or samples can be easily collected using tablets, smart phones, magnetic input, and so on. Writer-identification-based approaches can be categorized into two parts: text-independent and text-dependent. The text-independent methods analyze the entire image without knowing anything about the script's content, whereas text-dependent approaches analyze the entire image based on such knowledge. The summary of previous research on the identification of writers using handwriting is presented in Table 1. Only a few types of research on writer identification have been conducted on handwritten-based Kanji characters. In some sense, our research focuses on both online text-independent and text-dependent writer identification approaches for handwritten-based Kanji characters. In this study, we use x-coordinates, y-coordinates, pen pressure, pen altitude, pen azimuth, and the time taken to acquire the data. We collected online handwritten-based Kanji characters using a pen tablet. We extracted 47,520 samples from 33 people who wrote different 72 Kanji characters 20 times each and used efficient features to achieve a higher identification rate. Furthermore, we compared how the accuracy varies depending on whether the type is text-dependent or text-independent.  
The organization of this paper is as follows: Section 2 presents related work; Section 3 presents the materials and methods, including dataset preparation, proposed methodology, feature extraction, feature normalization, feature selection, and classification using SVM. The experimental results and discussion are discussed in Section 4. Finally, the conclusion and future work direction are discussed in Section 5.