## 2 Related Work  
Writer identification based on online Kanji characters can achieve a greater identification rate than offline Kanji characters because the online characters provide more information such as the length of the stroke order and brushstrokes. We reviewed various existing papers on writer identification, which were mainly conducted on only online data \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Authors** & **DT** & **SS** & **Methods** & **Language** & **Writers** & **ACC(\%)** \\ \hline [PERSON] et al. [9] & Online & 1230 & ANOVA & Kanji & 41 & 90.4 \\ [PERSON] et al. [11] & Offline & 5000 & Majority & Kanji & 100 & 99.0 \\ [PERSON] and [PERSON] [13] & Online & 400 & NN & English & 6 & 88.0 \\ [PERSON] et al. [14] & Online & 1500 & k-NN & English & 242 & 93.6 \\ [PERSON] et al. [15] & Online & 1700 & HMM & English & 200 & 95.5 \\ [PERSON] et al. [17] & Offline & 2965 & CNN & Kanji & 480 & 93.8 \\ [PERSON] et al. [6] & Online & 52,800 & P2P & Chinese & 48 & 99.0 \\ [PERSON] et al. [4] & Offline & 4800 & k-NN & Arabic & 82 & 90.2 \\ [PERSON] and [PERSON] [10] & offline & 50,000 & CNN & Japanese & 100 & 90.0 \\ \hline \hline \end{tabular} DT—data types; SS—sample size.  
\end{table} 
Table 1: Previous research on writer identification. or offline data types based on various handwritten characters, such as English, Arabic, Japanese, Chinese, and so on. We summarized these existing papers and presented in Table 1. Our study mainly focuses on writer identification based on online handwritten Kanji characters. Efficient feature extraction and feature selection play a significant role in writer identification. Various local features were extracted from the characters to show the discriminative information of the writer. For example, [PERSON] et al. [19] used four features (transition, diagonal, zoning, and peak-extent-based) in order to develop a writer identification system using Devanagari characters. [PERSON] and [PERSON] [20] proposed a novel feature selection approach for writer identification and obtained an identification rate of 96.0%. Moreover, some studies used both local and global features for writer identification [11; 21]. In existing studies, there have been various writer identification methods such as SVM [22; 23], distance-based [9; 24], deep learning [25], and so on. For example, [PERSON] et al. [9] used online text-independent data of handwritten-based Kanji characters and collected 1230 samples from 41 respondents. They extracted different kinds of features using a one-way analysis of variance (ANOVA). Moreover, the distance-based method was adopted to separate the writers and obtained an identification accuracy of 90.4%. [PERSON] et al. [11] utilized offline handwritten-based Kanji characters for writer identification and obtained 99.0% identification accuracy using a voting method for three Kanji characters. [PERSON] and [PERSON] [13] used a neural network (NN) to identify the writers using online handwritten-based English characters and achieved a classification accuracy of 88.0%. [PERSON] et al. [14] proposed k-nearest neighbors (k-NN) for writer identification based on online handwritten-based English characters and obtained an identification accuracy rate of 93.6%. [PERSON] et al. [15] employed a hidden Markov model (HMM) for the identification of writers and achieved a 95.5% identification accuracy rate. [PERSON] et al. [17] also adopted the convolution neural network (CNN)-based model for writer identification and reported 93.8% identification accuracy. [PERSON] et al. [6] also proposed deep learning for the identification of writers using handwritten-based Chinese characters. They collected 52,800 samples and obtained 99.0% identification accuracy. [PERSON] et al. [4] also proposed k-NN method for writer identification using offline handwritten-based Arabic characters and achieved 90.2% identification accuracy. [PERSON] and [PERSON] [10] conducted an experiment on Japanese handwritten characters that contained 100 Japanese characters written 50 times by 100 subjects.