## 3 Results and Discussion
### Some Visual Results  
For a qualitative view of the results, the Figure 5 shows the predictions of each trained YOLO model for four selected images from the test set. Analyzing the predictions, the following observations made:  
* In test image 1 (Figure 5a), it can be seen that YOLOv8 (Figure 5b) detects all the desired regions correctly, despite producing two false positives for the Text class: one at the bottom of the document, with 0.25 confidence, and another at the top left. YOLOv9 (Figure 5c), even though many regions on the page are well detected, it has limitations related to the fragmentation of text blocks, low confidence scores in specific areas and confusion between nearby classes. YOLOv10 (Figure 5d) was able to detect all regions, generating only one false positive, which proves its high precision, shown in Table 4. YOLOv11 (Figure 5e) shows a similar result to YOLOv8, but with three false positives from the Text class;
* In test image 2 (Figure 5f), which has a high number of instances, all of which are of the Text class, the YOLOv8 (Figure 5g) and YOLOv11 (Figure 5j) architectures performed better in the layout analysis task. However, it can be seen that YOLOv9 (Figure 5h) and YOLOv10 (Figure 5i) did not locate all the text regions, which is in line with the lower recall of these architectures. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Architecture** & **Recall** & **Precision** & **mAP50** \\ \hline
**YOLOv8** & **0.809** & 0.845 & **0.891** \\ YOLOv9 & 0.712 & 0.754 & 0.757 \\ YOLOv10 & 0.495 & **0.949** & 0.692 \\ YOLOv11 & 0.801 & 0.854 & 0.871 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the architectures’ performances. * In test image 3 (Figure 5k), it can be seen that YOLOv8 (Figure 5l) correctly identified all text regions, whether in the form of a paragraph, title or subtitle, as well as detecting the graphic element present on the page. Only a section of the subtitle was not detected by YOLOv8 and was left out of the bounding box that was generated for that text. YOLOv9 (Figure 5m) detected fewer layout elements, missing some parts of the text by producing inaccurate bounding boxes and giving rise to false positives for the graphic and text class. YOLOv10 (Figure 5n) and YOLOv11 (Figure 5o), in turn, achieved a  
Figure 5: Predictions made by the architectures on four test images, (**a**) Test image 1, (**b**) YOLOv8, (**c**) YOLOv9, (**d**) YOLOv10, (**e**) YOLOv11, (**f**) Test image 2, (**g**) YOLOv8, (**h**) YOLOv9, (**i**) YOLOv10, (**j**) YOLOv11, (**k**) Test image 3, (**l**) YOLOv8, (**m**) YOLOv9, (**n**) YOLOv10, (**o**) YOLOv11, (**p**) Test image 4, (**q**) YOLOv8, (**r**) YOLOv9, (**s**) YOLOv10, (**t**) YOLOv11. similar performance to YOLOv8, with YOLOv11's result showing bounding boxes better adjusted to the text blocks;
* In test image 4 (Figure 5p), YOLOv8 (Figure 5q) detected the blocks of text and the image on the newspaper page more accurately and consistently, with the bounding boxes delimiting the text columns well. YOLOv9 (Figure 5r) correctly recognized the image on the page, but did not detect all the text regions, once again underperforming YOLOv8. YOLOv10 (Figure 5s) had the worst result for test image 4 among the architectures, failing to detect several text regions just below the title. Once again, YOLOv11 (Figure 5t) performed closely to YOLOv8, capturing the text regions and the image on the page well. With regard to failure cases, it can be seen that the YOLO models analyzed reveal distinct patterns of error in the detection of layout elements in historical newspapers. YOLOv8, despite having the best overall performance, showed difficulty in correctly separating very close blocks of text, grouping distinct columns into a single region, which may be related to its sensitivity to textual density. YOLOv9 performed solidly but failed to detect graphics and images in some cases, ignoring these elements or identifying them incompletely, possibly due to its lower ability to capture non-textual visual patterns.