## 4 Results and Discussion
### Comparison of Our Proposed Identification System and Similar Existing Studies  
In this section, we made a comparison between our proposed writer identification system against similar existing studies. This comparative study is shown in Table 7, which presents various parameters such as author names, data type (DT), study type, sample size, classification methods, language, number of characters and writers, number of selected characters and writers, and identification accuracy rate. Some existing studies were conducted for writer identification using Kanji characters as follows: \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**SN** & **Kanji** & **ACC** & **SN** & **Kanji** & **ACC** & **SN** & **Kanji** & **ACC** & **SN** & **Kanji** & **ACC** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 4: Identification accuracy (in %) of SVM for each character. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Kanji** & **ACC** & **Cost** & **Gama** & **Kernel** \\ \hline
99.4 & 0.1 & 0.00001 & Linear \\
99.6 & 10 & 0.01 & RBF \\
99.4 & 0.1 & 0.00001 & Linear \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Identification accuracy (in %) of connected characters. [PERSON] et al. [9] collected a total of 1230 samples using pen tablets from 41 subjects (males: thirty-five; females: six). They asked each of the subjects to write only four Kanji characters five times, and repeated this six times (\(5\times 6\times 41\)). They extracted 563 features and evaluated their discriminative power using one-way ANOVA and Kruskal-Wallis test. They separated the writers in feature space based on distance and achieved an identification accuracy of 90.4% for all 563 features; an identification accuracy of 96.5% was also obtained for 270 selected features. [PERSON] and [PERSON] [24] also proposed a recognition system for text-dependent writer identification except using character recognition features. They obtained five features, such as start and end points of both x and y coordinates, and the angle of the stroke from each stroke. They conducted their study on 50,000 samples (\(100\times 100\times 50\)), which had 100 Kanji characters, 100 subjects, and 50 samples of each character's class for a writer. They used 5000 samples (100 writers \(\times\) 50 samples) for the experiment of each character class. They used 4999 samples for the dictionary and one sample for validation. Then, Euclidian distance was used to determine the writers and achieved an identification accuracy of 99.6%, 97.0%, and 95.2% for 10 writers, 50 writers, and 100 writers, respectively. Here, the authors obtained good identification accuracy when they considered only 10 writers. [PERSON] et al. [11] conducted another study on the same database [24] and proposed efficient character features and a recognition system for only text-dependent writer identification. They extracted various local and global features from each character. These extracted features were fed into a majority voting approach with a leave-one cross-validation protocol for writer identification and obtained an identification accuracy rate of 99.0% for three-character classes. [PERSON] et al. [17] proposed a CNN-based approach for offline text-independent writer identification. They illustrated that their proposed method \begin{table}
\begin{tabular}{c c c c} \hline \hline
**SN** & **Characters** & **SVM-WO-SFFS** & **SVM-W-SFFS** \\ \hline
1 & Top 5 & 96.2 & 99.0 \\
2 & Top 10 & 96.5 & 97.7 \\
3 & Top 15 & 96.0 & 97.0 \\
4 & Top 20 & 96.2 & 97.4 \\
5 & Top 25 & 95.6 & 97.1 \\
6 & Top 30 & 96.1 & 96.7 \\
7 & Top 35 & 95.6 & 96.9 \\
8 & Top 40 & 95.5 & 96.9 \\
9 & Top 45 & 95.5 & 96.3 \\
10 & Top 50 & 95.3 & 96.1 \\
11 & Top 55 & 94.7 & 95.9 \\
12 & Top 60 & 94.8 & 95.6 \\
13 & Top 65 & 94.2 & 95.3 \\
14 & Top 70 & 94.1 & 95.0 \\
15 & All 72 & 93.9 & 94.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Identification accuracy (in %) of Kanji characters used for each of the five letters.