## 2 Literature Review  
Currently, the open datasets described with stamps are quite limited [22], which makes it difficult to develop and train detection models. However, the literature presents some approaches based on machine learning and deep learning, which can be used as a starting point for future research.  
In the study by [23], the SFDL (spectral filtering-based deep learning algorithm) was proposed to detect logos and seals in scanned document images. The SFDL was used to separate the graphical regions of the image in the StaVer dataset. Candidate regions were classified using a deep convolutional neural network, achieving a precision of 94.7% and a recall of 85.8% for 300 dpi images. In [24], the authors employed a fully convolutional neural network to segment images into two classes, stamp and background. The model architecture is based on the pre-trained VGG-16, evaluated on the StaVer dataset, where it achieved a precision of 87% and a recall of 84%. The authors report that this method has presented difficulties in segmenting black stamps.  
In the approach proposed by [25], the authors present a compact neural network model called YOLO-Stamp, based on the YOLO network architecture for detection, with 128,000 parameters. To perform stamp detection, the proposed model was tested with the scanned pseudo-official data-set (SPODS) and achieved a precision of 90.6% and a recall of 90.7%. On the other hand, the research by [26] conceptualizes a stamp extraction network framework, based on a GAN (generative adversarial network), to extract texture features from images containing stamps. Accordingly, an optimized stamp text recognition method, PP-OCR, was proposed for text identification in stamps of various formats. Similarly, in the approach by [27], a verification detector was used, employing a CNN (Convolutional Neural Network) for the automatic segmentation of document elements. The results were compared with a Haar+Adaboost detector, highlighting the performance of the proposed models.  
In [4], a three-step approach for stamp verification was proposed using the StaVer dataset. The first step involved segmenting object areas, the second step used an SVM to classify the areas as stamp or non-stamp, achieving an accuracy of 90%. Finally, it verified whether the stamp was genuine or not. In the same direction, in the approach by [22], different versions of the YOLOv8 model were considered, including YOLOv8n, YOLOv8s, and YOLOv8m, with the goal of implementing an efficient and accurate stamp detector. To evaluate the implemented models, precision, recall, and mAP50 metrics were used, highlighting that the best result was achieved by YOLOv8n with a mAP of 98.63%.  
Thus, considering the rapid evolution of object detection methodologies, the most recent versions of the YOLO family (YOLOv9, YOLOv10, and YOLOv11), released in 2024, present significant advancements in accuracy, computational efficiency, and generalization. However, their application in stamp detection in documents has not yet been widely explored, representing a gap in the literature. Given their potential, these architectures offer relevant contributions compared to previous approaches and are being applied in this research.