# A Symbol Recognition System for Single-Line Diagrams Developed Using a Deep-Learning Approach
## 1 Introduction  
There are numerous industries in which engineering documents continue to exist in paper format due to a lack of digitalization for automated systems [1]. Among such documents, single-line diagrams (SLDs) pose a significant challenge in terms of interpretation and comprehension, as exemplified in Figure 1. These technical documents play a crucial role in diverse fields, including electrical systems, power distribution systems, hazardous area layouts, and other structural layouts. Deciphering these drawings often requires a considerable amount of time and the expertise of highly skilled engineers and professionals [2, 3]. The digitization of engineering drawings has gained significant importance in recent years. This is partially due to the pressing need to enhance business procedures, such as equipment monitoring, risk analysis, safety checks, and other operations. It is also driven by the remarkable advancements in computer vision and image understanding, particularly in the fields of gaming and AI [4], NLP [5], health [6], and others. Machine learning and deep learning (DL) [7] have significantly improved performance in various domains. Machine vision, in particular, has greatly benefited from DL [8; 9]. Convolutional neural networks (CNNs) have made remarkable progress in recent years and are widely used in various image-related tasks, including biometric-based authentication [10], image classification, handwriting recognition, and object recognition [11]. The advancements in image segmentation, classification, and object recognition prior to CNNs were incremental and limited. However, the introduction of CNNs has completely transformed this field [11]. For example, [PERSON] et al. introduced the DeepFace facial recognition system, which was first implemented on Facebook in 2014, achieving an accuracy of 97.35%, surpassing conventional systems by approximately 27% [12]. Despite notable advancements in image processing and analysis, the digitization of single-line diagrams (SLDs) and the automated interpretation of these drawings continue to pose significant challenges [13]. Presently, most methods rely on traditional image processing techniques that necessitate manual feature extraction. These approaches are highly domain-specific, susceptible to noise and data distribution variations, and tend to focus on addressing specific aspects of the problem, such as symbol detection or text separation. The performance of these models is greatly influenced by the quality of the provided training data. Even in challenging and less-controlled environments, fundamental image segmentation and other processing tasks, such as object detection and tracking, have become considerably less difficult. Recent methods, such as faster regions with convolutional neural networks (Faster R-CNNs) [13], single-stage detection (SSD) [14], region-based fully convolutional networks [15], and You Only Look Once (YOLO) [16], have demonstrated high performance in object recognition and classification applications. These methods,  
Figure 1: A single-line diagram. along with their extensions, have addressed major obstacles, such as noise, orientation, and image quality, leading to significant advancements in this field of study [17]. Insufficient datasets for the training process pose another significant challenge in the digitization of engineering documents [18]. Deep-learning models require vast numbers of data for effective training. Given the industry's heavy reliance on manual interpretation of these documents, there has been limited effort in generating the drawings automatically, making it challenging for researchers to acquire labeled data. To tackle this issue, data augmentation techniques have been introduced in recent years [19]. Generative models have also undergone significant advancement and have been effectively used in numerous applications. Generative adversarial networks have recently emerged as some of the most well-known and frequently employed tools for producing content.