## 2 Related Work
### Sketch Detection  
The basic motivation for symbol and drawing recognition in engineering was around for a long time, since the first approaches were published in the early 1980s. These methods are mostly based on rules for the recognition of symbols. Recognition involves locating and classifying the various objects in a drawing, with algorithms varying widely depending on the task and scope. In the more recent publication of [PERSON] et al. [7], the entire digitalization process is structured into two sections, the symbol detection and the contextualization. Contextualization aims to automatically reuse the detected symbols, for example, to find linked documents or to check the drawing for errors. A general overview of the developed methods is given in Figure 3. The two diagrams show the results of a literature search of over 200 publications in the field of symbol detection and contextualization in engineering, gathered from journal and conference papers from the last 40 years. All methods were classified according to their engineering domain, the use of a learning technique (Machine or Deep Learning) and the dataset type. The engineering domains describe the application scenario, whether it is located in the mechanical, chemical or architectural environment. The learning methods are differentiated according to the type of training or learning on data, whereby all supervised, semi-, weakly-supervised and unsupervised methods are classified as learning methods. The table with all references is provided in Table A4 in Appendix C, sorted by the engineering domain and year. The first diagram (Figure 3a) displays the distribution of developed methods per engineering domain. The bar chart demonstrates that many procedures were created for mechanical, civil, electrical and chemical engineering. In computer science and for the general methods, significantly fewer contributions were found. The reason is that only general methods in the context of the engineering domain are considered, e.g., the recognition of arrows or text in symbols. General methods for digitalization of drawings such as vectorization or Optical Character Recognition (OCR) are not included. In addition to the distribution among the disciplines, the number of publications over time is shown in Figure 3b in two-year increments. A differentiation was drawn between methods with and without a learning technique, which includes Machine and Deep Learning. For many years most approaches applied no learning techniques and focused on the use of conventional algorithms and heuristics. However, an increase in approaches with Machine or Deep learning is observed from the middle of the 2010s onwards. The expansion in publications over time can be explained by the development of new learning procedures (e.g., CNN or Visual Transformer) and growing computational performance. In addition to the chronological observation, another comparison of the literature is provided in the sunburst diagram in Figure 4. This shows the number of publications for the different categories of sketch digitalization. The analysis reveals that most of the methods focus on symbol detection and less on contextualization or preprocessing. Furthermore, the proportion of procedures with and without learning methods is almost similarly distributed across the entire study. Figure 3: Overview of the literature review results for symbol detection in engineering drawings. (**a**) Publication numbers sorted by engineering domain. (**b**) Chronological progression of publications. Learning methods have the disadvantage of requiring a large amount of labelled training data, which is why Figure 4 shows a breakdown of the individual solutions in the outermost circle. A closer look at the methods with learning reveals that many rely on their own dataset. This can be provided by industry partners or is self-generated through databases or scraping methods. Afterwards, these datasets must be manually labelled to be used for training the model.