## 3 Analysis of the System
### The System to Detect Shabtis
However, VSF uses an integral image on the original scale to obtain high quality features. Although VSF has been widely used in human detection extraction, it has produced excellent results in our problem, and less than 10% of the labels of the shabtis have been manually updated. The importance of saliency consists in the need for some DNN algorithms, such as YOLO, to know the limits of the objects that have to be recognized. In Figure 6c, the limits of the shabti of [PERSON] are obtained, detecting the first/last active pixels in the saliency map. The center of this region and the width and height are the parameters used with the image to train the network. Figure 5: Two shabtis for the same person, probably [PERSON]. [PERSON] dynasty (380 BC–343 BC), Late–Ptolemaic Period. Private collection. In addition to the figures database, the HN has been trained with the names of some of the shabtis of the main database, concretely 201 names for 60 different shabtis. These names have been manually labelled, since it is necessary to read the hieroglyphs and filter the concrete names. Some parts of the inscriptions refer to the name of the parents, the titles of the deceased (see Figure 7), religious expressions (see Figure 8) or even Chapter VI of the Book of the Dead in shabtis with several lines of text. Figure 9 shows the manual annotation of some of the names. Figure 6: Shabti of [PERSON] (1294–1279 BC) at MET (Metropolitan Museum of Art), New York. Figure 7: Hieroglyphic inscription, using the Gardiner code [40], representing the title of the King [PERSON] Private collection. The names found on two shabtis of the same person tend to be very similar, either because they were created through a mold, or because they were written in the same way, using the same hieroglyphics in the same position. In some cases, the name may appear in a different form or in different material on several groups of shabtis from the same tomb. However, the different cases of names have been included during the training. The two networks, FN and HN, have been implemented using YOLOv3. Two important aspects of YOLO are necessary for our project. One is a detector, which is necessary since our problem is an identification problem, and the other is the feature of returning the bounding boxes to compare the results obtained by FN and HN. In this way, it is possible to discern whether a name in hieroglyphics detected by the HN is within a shabti detected by the FN. Unlike other methods that use pipelines for detection or classification, YOLO [31] uses a single neural network. YOLO receives an image as input and returns a bounding box vector and the prediction percentage of the corresponding detected categories as output. The input image is divided into a grid of \(S\cdot S\) cells. For each object present in the image, the cell in which the center of the object is located is responsible for its prediction. Each grid cell predicts the bounding boxes, \(B\), and the class probabilities, \(C\). The output of the network consists of bounding boxes as well as class probabilities. The prediction of each bounding box has 5 components: \((x,y,w,h,confidence)\). The coordinates \((x,y)\) represent the center of the box, relative to the location of the grid cell; while \((w,h)\) are the weight and height of the box relative to the size of  
Figure 8: Hieroglyphic inscriptions, using the Gardiner code [40], represent some religious expressions in addition to the names. Private collection. Figure 9: Manual labelling of hieroglyph names for the HN. Private collection. the image. The confidence shows the certitude that an object of these dimensions is present at that position. In addition, in each cell, there are probabilities corresponding to each of the possible classes. YOLO uses a single CNN network with different convolutional, max pooling and full-connected layers. The convolutional layers extract features, called feature maps, and the pooling layers distill features down to the most salient elements.