## 3 Analysis of the System
### The System to Detect Shabtis  
The system is composed by two different parts. The first part is responsible for the detection of the shabti using computer vision. The second part obtains data from Europeana when a name has been detected. Figure 4 shows the schema of the system. Although a unique network could be trained to detect both kinds of object at the same time; in our work, we have preferred to decouple this process, so it is easier to make modifications or perform new trainings in each module separately. Moreover, the results are improved when two networks are responsible for detecting different elements. In our system, a first network, named Figures Network (FN), detects shabtis by their complete figure. A second network, named Hieroglyphs Network (HN), only detects Egyptian names written in hieroglyphs. There are two main reasons for using the concept of two different networks. On the one hand, some shabtis have clear hieroglyphs that are well repeated in different examples for a concrete person. It is important to note that, although it is possible to find two similar shabtis of two different persons with the same name, it is uncommon. If the style of two shabtis is very similar and the name is the same, it is likely to be the same owner. Moreover, some shabtis are broken and only some fragments have been found. In this case, the detection of the name in hieroglyphs is essential because a comparison between the figure and other complete examples is not possible. On the other hand, the inscription of some shabtis is illegible. These cases require the detection of the complete figure. In addition, some shabtis do not have an inscription or they do not have it on its front, such as the examples of [PERSON] (see Figure 3l) or [PERSON]. In these situations, the hieroglyphs detection makes no sense and the detection of the complete figure is required. One question that arises at this point is whether it is possible to detect the owner of a shabti by its figure or not. The answer is that many times it is. People who work in cataloguing shabtis are used to identifying the owner of a shabti in many situations just by looking at the figure and without reading the hieroglyphs. Some examples do not even have inscriptions, but are likely to be from the same owner, as shown in Figure 5. Going back to a previous example, the shabti of Seti I (see Figure 3a) is practically illegible because its text cannot be read well. Some cartouches with the name of the pharaoh can be seen, but it is impossible to read  
Figure 4: Schema of the system with some shabtis and names used for the training. Shabtis/Names from the MET (Metropolitan Museum of Art), New York, or Private collection. them correctly. However, it is clearly a shabti of Seti I of an excellent manufacture not seen in any of the other shabtis of Figure 3.  
A database with 1111 images and 151 different shabti owners has been created for the FN. Before training the Yolo network, a process has been carried out to extract the saliency of each image. This process automates the manual labelling of shabtis, which requires an enormous amount of time. The saliency corresponds to what stands out in a photo, focusing on the most important regions. As an example, the saliency of the shabti of Seti I (Figure 6a) is shown in Figure 6b. The saliency algorithm is based on the model presented by [PERSON] and [PERSON] [41], where the authors presented VSF (Visual Saliency Features), a method to extract features based on a biologically inspired attention system [42; 43] and providing fine grained feature maps and much more defined borders than other previous methods such as VOCUS. The VSF method implements the same filter windows as those used in VOCUS, which converts the original color image into grayscale and creates a Gaussian image pyramid, applying a 3 \(\times\) 3 Gaussian filter and a scaling down factor four times consecutively. Finally, the system only takes into account the information present in the smallest scales.