## 3 Experimental Results and Discussion
### Experiment-1: Evaluation for Handwritten Text Dataset  
In this experiment, we used different types of handwritten texts and then extracted various types of features from each image or task. We applied SVM and RF classifiers to classify adult and child and calculated the classification accuracy. We used 30 hiragana words and extracted 30 features which are clearly explained in Table 3. Table 5 shows the performance scores of SVM and RF for better features combination of handwritten text dataset. It was observed that SVM with RBF kernel produced the classification accuracy of 87.7% for the combination of 15 selected features out of 30 features. Moreover, SVM also produced 92.4% recall, 85.9% precision, 89.1% f1-score, and 0.919 AUC for the selected 15 features, whereas RF classifier achieved an excellent classification accuracy of 93.5% along with 95.7% recall, 92.2% precision, 93.9% f1-score, and 0.983 AUC, respectively, for the combination of 18 selected features. Therefore, RF achieved more outstanding performance than SVM.  
We observed that 15 and 18 features were selected by SFFS with SVM and RF classifiers. A total of 11 common features was extracted from those two methods, which are shown in Figure 4, and the listed selected features are presented in Table 6. These 11 common features were used as input features and then we applied SVM and RF classifiers to distinguish adults from children.  
The performance scores of SVM and RF classifiers for 11 common features are shown in Table 7. It was observed that SVM with RBF provided 87.4% accuracy, 90.8% recall, 86.6% precision, 88.7% f1-score, and 0.947 AUC, respectively, whereas RF gave 91.5% accuracy, 93.0% recall, 91.5% precision, 92.3% f1-score, and 0.967 AUC, receptively. Finally, we may conclude that RF had more outstanding performance scores than SVM for the prediction of the adult and child for handwritten text dataset.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Variables** & **Overall** & **Adult** & **Child** & **\(p\)-Value1** \\ \hline
**Handwritten text dataset** & & & & & \\ Total, \(n\) (\%) & 57 & 26 (45.6) & 31 (54.4) & – \\ Gender, Female, \(n\) (\%) & 27 (47.4) & 11 (42.7) & 16 (59.3) & 0.483 \\ Age (year), Mean \(\pm\) SD & 19.2 \(\pm\) 10.2 & 27.3 \(\pm\) 10.5 & 12.5 \(\pm\) 0.3 & 0.001 \\ \hline
**Handwritten pattern dataset** & & & & \\ Total, \(n\) (\%) & 81 & 42 (51.9) & 39 (48.1) & – \\ Gender, Female, \(n\) (\%) & 48 (59.3) & 31 (64.6) & 17 (35.4) & 0.0025 \\ Age (year), Mean \(\pm\) SD & 17.8 \(\pm\) 7.9 & 23.9 \(\pm\) 4.9 & 11.8 \(\pm\) 1.6 & 0.001 \\ \hline \hline \end{tabular} \(n\) is the total number of subjects,1\(p\)-value is obtained from \(t\)-test for age and chi-square test for gender.  
\end{table}
Table 4: Baseline characteristics of adult and child.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**CT** & **\# of Features** & **ACC** & **Rec** & **Prec** & **f1-Score** & **AUC** \\ \hline SVM & 15 & 87.7 & 92.4 & 85.9 & 89.1 & 0.919 \\ RF & 18 & 93.5 & 95.7 & 92.2 & 93.9 & 0.983 \\ \hline CT: Classifier types. & & & & & \\ \end{tabular}
\end{table}
Table 5: Performance scores of SVM and RF for handwritten text dataset.