## 4 Conclusions and Future Works  
This paper proposes a dataset and presents experiments aimed at analyzing the layout of historical documents using deep learning techniques, more specifically, object detection algorithms. Using the roboflow, a dataset of Brazilian newspapers called BHN was developed, containing 140 images of document pages and ground truth for layout detection. Next, three historical newspaper datasets were combined, including the BHN, and a combined dataset was built, with a larger number of images. This resulted in a dataset with 459 images, which were separated into 321 training samples, 69 validation samples and 69 test samples. Four state-of-the-art object detection architectures were selected for this study. The algorithms were trained on the combined dataset and then evaluated on the test subset, where YOLOv8 was found to have the best performance. A significant mAP of 89% was obtained for YOLOv8. This model was able to correctly extract a large amount of information present in the documents, mainly texts and images, which were the two most detected classes. The introduction of the BHN dataset and the combination with other historical datasets allowed for a more robust analysis of the capabilities of these architectures. The BHN stands out as the first dataset aimed at detecting layout in Brazilian historical newspapers, filling a gap in the literature and offering an unprecedented resource for the scientific community. Figure 6: YOLOv8 confusion matrix. In addition to contributing to research in computer vision and machine learning, the BHN has direct applications in the preservation and digitization of the Brazilian historical press. Its use can facilitate the automatic indexing of digitized newspapers, improve OCR systems and enable more efficient searches in historical collections, benefiting researchers and institutions dedicated to document preservation. Therefore, in addition to its academic impact, the results of this work have direct implications for real-world applications. The accurate detection of historical newspaper layouts is a fundamental step towards the automated conversion of these documents into searchable and accessible formats. In large-scale projects, such as digital repositories and national libraries, the adoption of robust detection models can significantly speed up the digitization and indexing processes. In addition, integrating these models with OCR systems can improve automatic text extraction, reducing errors associated with inaccurate detections and contributing to better information retrieval. It is also worth noting that the proposed methodology can also be applied to other datasets, even contemporary documents, and would be useful for solving various problems in public and private institutions, in the sense of automating information recognition tasks in digital or digitized documents. A platform for transcribing the text from the image of the document could also be implemented to facilitate research into historical documents by scholars in the fields of history and archaeology, for example. For future studies, there is interest in the idea of carrying out a new experiment with transformer-based detection architectures. It would also be interesting to investigate the use of data augmentation techniques to increase the number of images in the dataset and possibly improve performance even further. Another promising idea for scientific advancement in the area is investigation of the impact of the layout analysis step on the final performance of engines OCR open source (such as Tesseract and OCROpy). Conceptualization, E.S.d.S.J. and A.B.A. ; methodology, E.S.d.S.J. ; software, E.S.d.S.J. ; validation, E.S.d.S.J., T.P. and A.B.A. ; formal analysis, E.S.d.S.J. ; investigation, E.S.d.S.J., T.P. and A.B.A. ; resources, A.B.A. ; data curation, E.S.d.S.J. ; writing--original draft preparation, E.S.d.S.J. and T.P. ; writing--review and editing, E.S.d.S.J., T.P. and A.B.A. ; visualization, E.S.d.S.J. ; supervision, A.B.A. ; project administration, T.P. and A.B.A.