## 3 Related Work  
An online repository of CDs was proposed by [3]. They proposed 23 image features and investigated the use of these features to classify UML CD images. Six classification algorithms were used on a set of 1300 images. They found that 19 out of 23 introduced features can be considered as influential predictors for classifying UML CD images. Enriching such a repository with images was quite easier because of the availability of a large number of UML CDs as images on the Internet. However, the problem with the above-mentioned approach was the proper classification of the images according to the relevance. [PERSON] et al. [23] proposed a content-based and keyword-based retrieval of models from repositories. This proves to be good testimony of the importance of retrieval techniques and repositories in model-driven engineering. However, this research is focused only on textual elements of models, i.e., the models are needed to be stored in different textual formats, such as XML (Extensible Markup Language) or its derivations (such as XMI (XML Metadata Interchange). These do not consider the model interpretation stored as images. A recent and substantial study of UML models stored in different formats in GitHub, combining a lot of manual work and automatic processing recognized 93.596 UML models from 24,717 different repositories, of which 61.8% (57,822) are images, the rest being files with extensions, XML or UML [24]. These data confirm that the existence of interesting and potentially useful information in repositories are not up to the mark which makes it difficult to access and reuse; this also gives the idea that a huge proportion of such models are stored as images [10]. The interest in automatic recognition of UML diagrams in repositories dates back to the times of appearance of the UML, with emphasis on sketch recognition in the software design cycle, to make available the information they contained to CASE tools [1]. The MDD/MDE (Model-Driven Development/Model-Driven Engineering) community supports and motivates the effective reuse of modeling artifacts and managing vast repositories, and this has increased the need for the identification of tool-made diagrams [12]. According to our knowledge, there is much more to explore in this area by the researchers. The research done in this field is quite scarce with only a few using Machine Learning techniques. The majority of the approaches use image processing and pattern recognition  
Figure 2: ReLU activation function. methods modified according to specific domains, such as architectural diagrams [25] or finite-state automata diagrams [26]. In the field of UML diagrams, [PERSON] and [PERSON] have research published about the same problem [27], although they have not used any automatic learning approach, but used a fixed set of classification criteria. Their Img2 UML tool [16] uses the Aforge.NET framework which is an open-source C# framework designed for researchers and developers in the fields of Artificial Intelligence, Neural Networks, Machine Learning, robotics, computer vision, image processing, genetic algorithms, fuzzy logic, etc. The UML class models are extracted from pixmap images and exported into XMI files by the tool and those files can be read by a commercial CASE tool (StarUML). A private repository is created with the collected images from the internet along with their XMI extraction [28]. Although this work has covered a broader aspect of the problem in this field still the tool does not solve all the problems which have been identified by them (specified identification of relationships, OCR recognition, and lack of XMI standardization). They used a few images to authenticate their tool (10 and 200 images in [27] and [28], respectively) and giving an accuracy of 89%. In the work of [PERSON] et al.