## 4 Experimental Results and Discussion
### Evaluation of the Proposed Method with Other Existing Methods
The classification result for the SVM-based method in Figure 14e was obtained in the same procedure, but the SVM models were trained using the appearance, cross-section, and local extrema features extracted from the surface of the mesh partitions. From visual inspection, the classification results obtained from the machine learning-based methods involving SVM and RF models were promising when compared to other existing methods, as illustrated in Figure 14e,f. This indicates that the geometrical features extracted in the SVM-based method contained useful information for identifying reliefs and background, and this observation is the same in the proposed RF-based method involving CSI features. It is difficult to quantitatively  
Figure 13: The comparison of the proposed method and existing methods: (**a**) mesh partitions; (**b**) ground truth; (**c**) the height-based method [8]; (**d**) the curvature-based method [7]; (**e**) the modified curvature-based method [18]; and (**f**) the SVM-based method [24]. (**g**) The proposed method. analyze the performance of these methods on the entire stele because there is no ground truth label for the 113 mesh partitions. Figure 14: The comparison of the existing relief extraction methods and the proposed method: (**a**) stone rubbing; (**b**) the height-based method [8]; (**c**) the curvature-based method [7]; (**d**) the modified curvature-based method [18]; (**e**) the SVM-based method [24]; and (**f**) the proposed method. The performance of the machine learning methods, based on SVM and RF (with and without \(k\)-NN), were further evaluated using a fold containing the same 14 mesh partitions selected as test data. These machine learning methods were considered for comparison since their classification results are relatively similar. The comparison of the performance of these methods is presented in Table 6. The initial classification result obtained using the proposed method without \(k\)-NN, denoted as RF(CSI), is better than the SVM-based method in all metrics except recall. To further improve the reliability of the proposed method, the initial classification results is refined with the \(k\)-NN. As such, the proposed method, RF(CSI), refined with \(k\)-NN leads the SVM-based method with an increase of approximately 0.68%, 3.53%, 0.18%, and 2.25% in accuracy, precision, recall, and F1 score, respectively. Figure 15 depicts the objective comparison of these methods in terms of accuracy, precision, recall, F1 score, and segmented inscription recognition index (SIRI) [35]. The SIRI can evaluate the inscriptions extracted from the 3D scanning data by imitating human subjective inscription recognition. If a region slightly thinner or thicker than the actual relief segment is detected, it has little effect on the perceived quality of the inscription. In contrast, unexpected misdetection makes the perceived quality degrade significantly. Based on these observations, SIRI evaluates the quality of extracted inscriptions with different importance with respect to region. For the height-based method, false detection reduces precision and F1 score. The curvature-based method yields a number of false detections, resulting in much lower precision and F1 score. With the modified curvature-based method, all metrics increased but results are not acceptable. The SVM-based method significantly increases in precision, F1 score, and SIRI. The proposed method achieves the highest performance in all metrics except recall. The precision value improves by 0.0353 as compared to the second best method, which also leads to 2.66% higher F1 score. It is noteworthy that the proposed method achieves a larger improvement of 3.2% in SIRI. That indicates that the proposed method produces more recognizable characters, as confirmed in Figure 14f.