## 3 Experimental Results and Discussion
### Experiment-2: Evaluation for Handwritten Pattern Dataset  
To evaluate our proposed model, we used a handwritten pattern dataset and obtained a classification accuracy of up to 89.8%. In this section, we performed two experiments. Firstly, the best combination of the features set was identified using SFFS-based RF and SVM classifiers. We chose the feature combination at which the classification model provides the highest classification accuracy. The classification accuracy of RF and SVM for the handwritten pattern dataset is presented in Table 8. For the trace of zigzag lines, RF produced 83.3% classification accuracy for the combination of 19 selected features, whereas SVM produced 71.4% accuracy for the combination of 26 selected features. For the prediction of the zigzag, the RF classifier obtained the highest classification accuracy of 85.7% for 13 combinations of feature sets and the prediction of the zigzag line, whereas SVM provided 75.5% classification accuracy for 3 selected features. For the prediction and trace of the PL line, RF achieved 73.5% classification accuracy for the combination of 24 selected features, whereas SVM achieved 79.6% accuracy for 7 selected features and 87.7% accuracy for 12 selected features. RF classifier provided a good classification accuracy of 85.6% for the combination of all handwritten patterns, 25 features, whereas 82.1% classification accuracy was provided by SVM for the combination of all 28 features. Therefore, RF achieved better classification accuracy (89.8%) than SVM for the prediction of PL line.  
\begin{table}
\begin{tabular}{l l l l} \hline
**SN** & **Feature Names** & **SN** & **Feature Names** \\ \hline
1 & Height & 7 & GripAngleSDW \\
2 & Velocity & 8 & GripAngleSDL \\
3 & PIVL & 9 & PressureMean \\
4 & PIAL & 10 & PressureSD \\
5 & GripAngleMeanW & 11 & PCMin \\
6 & GripAngleMeanL & – & – \\ \hline \end{tabular}
\end{table}
Table 6: List of common features selected for handwritten text dataset.  
Figure 4: Identification of common features from SFFS-RF and SFFS-SVM for handwritten text dataset.  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**CT** & **ACC** & **Rec** & **Prec** & **f1-Score** & **AUC** \\ \hline SVM & 87.4 & 90.8 & 86.6 & 88.7 & 0.947 \\ RF & 91.5 & 93.0 & 91.5 & 92.3 & 0.967 \\ \hline \end{tabular}
\end{table}
Table 7: Performance scores of SVM and RF for common features for handwritten text dataset.  
The classification accuracies of RF and SVM for these common features are presented in Table 10. It was observed that the RF classifier provided a higher classification accuracy of 79.5%, 73.4%, 83.6%, and 89.8% for the trace and prediction of the zigzag line than SVM for the trace and prediction of the PL line, respectively. On the other hand, the SVM classifier provided 85.1% accuracy for all handwritten patterns, whereas RF classifier gave 84.1% accuracy.  
The recall, precision, f1-score, and AUC of RF and SVM for common features of the handwritten dataset are presented in Table 11. It was observed that the RF classifier achieved comparatively better performance for all types of lines than SVM. RF classifier provided a higher recall of 87.0%, precision of 93.1%, f1-score of 90.0%, and AUC of 0.903 for the prediction of the PL line dataset, whereas SVM gave 83.8% recall, and 0.811 AUC, respectively. Table 11 shows that the highest performance scores are achieved by RF for four types of lines with all handwritten patterns. Finally, we can say that in our experiment, RF performed better than SVM.