Comparative Performance of YOLOv8, YOLOv9, YOLOv10, and YOLOv11 for Layout Analysis of Historical Documents Images  
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column widths, presence of column dividers, etc. [8]. According to [9], most of the algorithms described in the literature were developed to recognize contemporary documents, such as magazines and books, with more regular layouts and fonts. For this reason, existing open-source OCR systems, such as Tesseract and EasyOCR, are unable to correctly identify the text regions of historical newspapers, thus jeopardizing the final OCR result [6]. Related studies show that in recent years, deep learning-based methods have become the most popular among researchers for document layout analysis [10]. The use of advanced architectures such as YOLO models, for example, in object detection tasks offers a powerful combination of precision and speed [11; 12; 13], crucial for layout analysis. The following studies are worth highlighting: ref. [14] used the approach of Faster R-CNN and Mask R-CNN as a unified framework graphical object detection for the analysis of graphics layout; ref. [15] used Siamese networks that consist of two identical convolutional neural networks to detect Arabic manuscripts; ref. [16] proposed LayoutLM, an algorithm capable of jointly modeling interactions between text and layout information across scanned document images; ref. [17] proposed DocFormer, a multi-modal transformer-based architecture for the task of Visual Document Understanding; ref. [18] proposed LayoutLMv3, the first multimodal model that does not rely on a pre-trained CNN or Faster R-CNN to extract visual features; [19] presented DiffusionDet, a new framework that formulates object detection as a denoising diffusion process from noisy boxes to object boxes; ref. [20] proposed the Real-Time Detection Transformer (RT-DETR), a transformer-based object detector for real-time that avoids the use of NMS; ref. [21] introduced DocLayout-YOLO, a novel approach that enhances accuracy while maintaining speed advantages through document-specific optimizations in both pre-training and model design; ref. [22] proposed DocFusion, a lightweight generative model for document parsing tasks; ref. [23] performed a comparative analysis between the YOLOv5s and YOLOv5m architectures applied to a custom dataset containing mainly images from books in English and Arabic; ref. [24] applied a high-accuracy YOLOv8 model to three document image datasets: ICDAR-POD, PubLayNet, and IIIT-AR-13K; ref. [25] proposed a single-stage DLA model multi-scale cross fusion former called YOLOLLayout; ref. [26] trained and evaluated a model for layout detection and OCR of historical documents from the 19 th century, and the proposed method was compared with the standard Tesseract OCR engine; ref. [27] proposed EDDocNet, a lightweight convolutional neural network composed of multiple depth separable layers to analyze electronic device documents. More recently, transformer-based architectures, such as DocFormer and LayoutLM, have also emerged as a promising alternative [10]. However, YOLO-based models offer significant benefits, such as real-time inference and lower computational complexity, making them more suitable for applications where speed is of the essence. In addition, the need for large volumes of labeled data to train transformers can be a barrier for historical documents, where the amount of data available is reduced. While transformers require large volumes of annotated data for satisfactory performance, YOLO is more robust for smaller sets. Additionally, models such as LayoutLM are more geared towards tasks involving semantic interpretation and text extraction while YOLO specializes in the efficient detection of objects within images. In this context, a limited number of related studies on layout analysis in German-Brazilian historical documents using deep learning techniques is observed.