## 2 Materials and Methods
### Used Architectures  
The YOLOv8, YOLOv9, YOLOv10, and YOLOv11 architectures were selected for use due to their proven performance capabilities in object detection and versatility in different scenarios. YOLO models are known for their speed in detecting objects compared to other detection methods, such as transformers, which generally require more computing power and inference time. This is essential for large-scale digitization scenarios. \begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Count** \\ \hline Text & 14.333 \\ Image & 23 \\ Graphic & 462 \\ Table & 42 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classes evaluated in the experiments. Figure 3: Sample images from the dataset. For real-time tasks or those involving large volumes of historical documents, the efficiency of YOLO can be a significant advantage. Furthermore, in the case of historical documents, where data are scarce and noisy, the use of YOLO allows for a simpler and more efficient pipeline for training and inference. Thus, YOLO models were chosen for their speed, simplicity and suitability for the task of layout detection. Some of the characteristics of each architecture are described below:
* **YOLOv8:** This state-of-the-art algorithm can be used for object detection, image classification, and instance segmentation tasks [32]. YOLOv8 was developed by Ultralytics, which included several changes to the architecture and developer experience, as well as improvements over YOLOv5. YOLOv8 uses an anchor-free model with a decoupled head to independently process the objectivity, classification and regression tasks. This design allows each branch to concentrate on its task and improves the overall accuracy of the model. In the output layer of YOLOv8, the authors used the sigmoid function as the activation function for the objectivity score, representing the probability that the bounding box contains an object. * **YOLOv9:** YOLOv9 was launched in February 2024 [33] as an update to previous versions. YOLOv9 boasts two key innovations: the Programmable Gradient Information (PGI) framework and the Generalized Efficient Layer Aggregation Network (GELAN). The PGI framework aims to address the issue of information bottlenecks, inherent in deep neural networks in addition to enabling deep supervision mechanisms to be compatible with lightweight architectures. By implementing PGI, both lightweight and deep architectures can leverage substantial improvements in accuracy, as PGI mandates reliable gradient information during training, thus enhancing the architecture's capacity to learn and make accurate predictions. * **YOLOv10:** YOLOv10 [34] sets a new standard in real-time object detection by addressing the shortcomings of previous YOLO versions and incorporating innovative design strategies. Its ability to deliver high accuracy with low computational cost makes it an ideal choice for a wide range of real-world applications. To address the _Non-Maximum Suppression_ (NMS) dependency and architectural inefficiencies of past YOLO versions, YOLOv10 introduces consistent dual assignments for training without NMS and a holistic model design strategy aimed at efficiency and accuracy. Thus, its main features are NMS-free training, holistic model design, and enhanced model features. * **YOLOv11:** According to [35], YOLOv11 represents the latest advancement in the YOLO family, building on the strengths of its predecessors while introducing innovative features and optimizations aimed at enhancing performance across various computer vision tasks. The YOLOv11 architecture has optimized feature extraction capabilities, making it possible to capture intricate details in images [36]. This model supports a range of applications, including real-time object detection, instance segmentation, and pose estimation, which allows it to accurately identify objects regardless of their orientation, scale, or size, making it versatile for industries like agriculture and surveillance.