## 4 Experimental Results
### Robustness Analysis  
To simulate possible distortions introduced during the image scanning process, disturbance scenarios were considered through the preprocessing of the images. These disturbances include low image resolution, the presence of Gaussian noise, and lighting variation with both low and high exposure, representing the five scenarios shown in the columns of Figure 11, respectively. Specifically, the procedure used to obtain the low-resolution image reduces and compresses the image with 80% lossy compression, resulting in a size of 270 \(\times\) 380. The additive Gaussian noise, with a mean of 0 and a standard deviation of 40, simulates moderate graininess in the image. The rotation scenario tilts the samples by an angle of 10\({}^{\circ}\). In order to test the robustness of the YOLOv9s model, which achieved the best quantitative and qualitative performance results, two samples from the test set of the dataset were considered, referred to as sample 1 and sample 2. In Figure 12, the model's predictions for both samples are shown. Based on this, the disturbance scenarios, presented earlier, were introduced to these samples in order to compare the performance of the predictions, as shown in Figure 13. The rows represent the samples, and the columns illustrate the disturbance scenarios, along with the results of the YOLOv9s model predictions. The results achieved for a low-resolution image in Scenario 1, shown in Figure 13a, illustrate that the model detected the circular stamp from sample 1 with a high confidence score, but it was unable to detect the rectangular stamp present in the image. For sample 2, the model also managed to perform detection but with a reduced confidence score. Figure 11: Pre-processed disturbances in the images: (**a**) Original. (**b**) With low resolution. (**c**) With Gaussian noise. (**d**) With shadow simulation. (**e**) With high light exposure simulation. (**f**) With rotation. In Scenario 2, as seen in Figure 13b, when Gaussian noise is present, the model was only able to locate the circular stamp from sample 1, while the second stamp, along with the stamp from sample 2, were not detected. For the prediction on images with low luminosity variation, Scenario 3 presented in Figure 13c, the model detects all stamps, providing a high confidence score for the circular stamp from sample 1 and the stamp from sample 2, showing only a low confidence score for the rectangular stamp located in the darker region of the image. Figure 12: YOLOv9s detection in two samples chosen from the test dataset, before applying disturbance scenarios. Figure 13: Predictions of the trained YOLOv9s model on two samples in various disturbance scenarios: (**a**) With low resolution. (**b**) With Gaussian noise. (**c**) With shadow simulation. (**d**) With high light exposure simulation. (**e**) With rotation. In Scenario 4, shown in Figure 13d, a similar behavior to Scenario 3 is observed, with high confidence scores for the detected stamps and difficulty only for the rectangular stamp from sample 1, due to the loss of its characteristics caused by high light exposure. Finally, Scenario 5, illustrated in Figure 13e, presents a rotation in both samples, where it is observed that the model was able to detect all the stamps present, maintaining a high confidence score. Thus, considering the results for the five disturbance scenarios, the greatest challenge for the evaluated model was observed when the stamp was fully overlapped by text and in Scenario 2, where the addition of noise made pattern recognition more difficult for stamp classification. To complement the robustness analysis of the YOLOv9s model, a real-world test was conducted using three document samples from the StaVer dataset, which did not contain any stamps and were not part of the adapted dataset used for model training. These samples were printed, manually stamped, scanned, and then, predictions were made by the model, as shown in Figure 14.  
The model was able to locate all the stamps present in Figure 14a,c.