# Egyptian Hieroglyphs Segmentation with Convolutional Neural Networks
## 1 Introduction  
The applications of the _Artificial Intelligence_ (AI) are permeating almost any field of science, from chemistry and physics [1; 2] to healthcare [3] and space research [4]. In recent years, this exponentially growing pattern can be primarily attributed to the particular class of algorithms known as Neural Networks or Deep Learning [5] (a sub-field of AI), which are adaptable to a variety of situations and well-suited to a data-driven approach to problem solving. More generally, the success of these methods has been also made possible by the advances in both computational power and telecommunications, which allow developers to share huge datasets and computational resources [6; 7].  
Computer vision is undoubtedly one of the scientific disciplines that has been most impacted by AI's advancements; currently, the vast majority of its applications are based on a specific class of Deep Neural Networks called _Convolutional Neural Networks_ (CNN). Many of the issues in this subject can be solved by CNNs, including detection, recognition and segmentation tasks, as well as images generation [8; 9; 10; 11].  
The deployment of large neural network architectures capable of facing such tasks resulted in a horizontal spread of these technologies across many different fields, such as clinical imaging [12; 13; 14] or cultural heritage [15]. In the latter field, solutions were proposed, for example, for the recognition of the old Kuzushiji Japanese writing style [16; 17], Maya [18] and Egyptian hieroglyphs classification [19; 20]. These applications can be a verychallenging test for CNNs, due to the complexity of the problem deriving from the artifacts' state of conservation or the high variability of documents [21; 22; 23]. Some of these challenges will be discussed later in this article.  
In this work, we evaluate the application of the well-known framework Detectron2 [24; 25; 26] when applied to the study of ancient Egyptian hieroglyphs, with the final goal of offering a solution to the segmentation task. Problems related to the creation of a well-suited dataset to train a CNN are described. In addition, some limitations, mainly related to the dimensions and quality of images, are also pointed out.  
Segmentation is a fundamental step in several hieroglyphs analysis tasks: for example, it is the starting point for symbol transliteration and, subsequently, for language translation; at the same time, it may open the possibility to study the similarities between glyphs, moving towards the identification of the so-called \"hand of the scribe\". Therefore, another objective of this study may be seen as investigating the role of CNNs supporting the work of Egyptologists.  
The paper is organised as follows: In Section 2, we address the complexity of the hieroglyphic writing system, in order to highlight the challenging aspects that any Deep Learning application might encounter. Then, in Section 3, we describe the main features and the reasons why we opted to use Detectron2, followed by an explanation of our dataset creation procedure. In Sections 4 and 5, we provide the results obtained on the validation and test sets and on images taken from public datasets. Concluding remarks are presented in Sections 6.