## 5 Conclusions  
This paper presents a performance evaluation for stamp detection in scanned documents using deep learning models. Experiments were conducted with the following four architectures from the YOLO family: YOLOv8s, YOLOv9s, YOLOv10s, and YOLOv11s. The dataset used in this research was adapted from the StaVer and DDI-100 datasets, and the robustness of the best-performing model was assessed under conditions of low resolution, noise, low and high lighting exposure, and rotation.  
The quantitative evaluation was based on object detection metrics such as precision, recall, and mAP. The YOLOv9s model obtained the best results, standing out for its greater robustness compared to YOLOv8s and YOLOv10s, which had similar mAP values. In addition, YOLOv9s outperformed other approaches in the literature for stamp detection.  
Figure 14: Predictions of the YOLOv9s model in real tests: (**a**) Document containing two stamps. (**b**) Document containing two stamps. (**c**) Document containing one stamp.  
However, when evaluating the complexity of the architecture and the inference time of the predictions, YOLOv11s performs better than the other models.  
A qualitative analysis was performed with samples from the test set in order to demonstrate the performance and compare the predictions of the models and their confidence scores for each detection. Considering the detection of all stamps present in the documents, the results of the analysis demonstrated that the YOLOv9s model presented the best behavior, showing agreement with the results obtained in the quantitative analysis. The robustness of the best trained model was evaluated, considering the presence of disturbances in five scenarios. The model's predictions indicate the ability of the YOLOv9s model in the detection task even in the presence of disturbances, showing only a limitation when there is the presence of noise that makes it difficult to recognize and classify the stamps. Finally, an analysis of a real case reveals significant performance in detections, suggesting that future integration with preprocessing techniques could further improve the results obtained.  
Based on the analyses conducted, it is possible to determine the most suitable model for a given scenario, depending on the context required for object detection. In terms of both quantitative and qualitative performance, YOLOv9s achieved the best results. Regarding computational cost, YOLOv11s stands out due to its lightweight architecture and fast inference speed. Meanwhile, YOLOv8s offers a balance between high performance and low inference time. This comparative study contributes to the state of the art by identifying the most effective approach for stamp detection in digitized documents.  
For future work, the aim is to increase the diversity of the dataset, apply integrated pre- and post-processing techniques to enhance predictions, conduct a detailed statistical analysis including confidence intervals, modify YOLO architectures for stamp identification, and outline the steps for implementing an automated stamp authenticator.  
Conceptualization, J.B., T.P. and A.B.A.; methodology, J.B.; software, J.B.; validation, J.B., T.P. and A.B.A.; formal analysis, J.B.; investigation, J.B., T.P. and A.B.A.; resources, A.B.A.; data curation, J.B.; writing--original draft preparation, J.B. and T.P.; writing--review and editing, J.B., T.P. and A.B.A.; visualization, J.B.; supervision, A.B.A.; project administration, T.P. and A.B.A.; funding acquisition, A.B.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the PAVIC Laboratory, University of Acre, Brazil.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors gratefully acknowledge support from the PAVIC Laboratory, benefited from the SUFRAMA fiscal incentives under Brazilian Law No. 8387/1991.  
The authors declare no conflicts of interest.