# A Symbol Recognition System for Single-Line Diagrams Developed Using a Deep-Learning Approach
## 2 Related Works
### Digitization of Engineering Documents  
Engineering drawings commonly incorporate diverse forms, symbolic shapes, solid or dashed lines, and text to depict intricate engineering processes in a condensed and comprehensive manner. These technical drawings find extensive application across multiple disciplines. Over the past decade, considerable research efforts have been dedicated to machine vision with the aim of digitizing these sketches [22; 23; 24; 25]. With remarkable progress in computer vision and machine learning, coupled with the existence of vast numbers of undigitized legacy data, the need for a fully automated framework to digitize these drawings has become more imperative than ever before. One primary limitation of learning methods is their heavy reliance on extensive feature extraction, which is highly dependent on the quality of the extracted features and often lacks generalizability to unobserved instances [26]. The existing literature in this domain has primarily focused on addressing specific aspects of digitizing engineering diagrams, rather than providing a comprehensive and fully automated framework, as indicated by a recent comprehensive review [27]. Some studies have concentrated on the identification and categorization of common symbols in engineering drawings, as well as the separation of text from other graphical elements in diagrams, employing image processing techniques for line identification and deep-learning methods for symbol detection [28]. Alternatively, heuristic strategies have been employed in other studies to locate and classify components in drawings using approaches such as random forests, achieving precision levels exceeding 90% [29], or to discern and differentiate text from graphic elements [30; 31; 32; 33]. However, these approaches may require the modification of heuristic rules or the development of new ones when there are changes to the schematic or symbol representations [34]. Additionally, the effectiveness of these approaches heavily relies on a balanced distribution of data across the dataset. In recent years, attempts have been made to apply deep-learning-based techniques to tasks similar to the digitization of engineering drawings [35]. Some studies have utilized techniques based on single-stage detection (SSD) to identify doors, windows, and furniture objects in floor-plan diagrams, yielding positive results [36]. However, these studies used small datasets with an insufficient number of furniture items in each drawing [37]. The unbalanced distribution of object classes within these datasets leads to a decline in performance [38; 39]. Symbol recognition in process and instrumentation diagrams (P&IDs) is a closely related field. The complexity of P&IDs introduces various challenges in symbol recognition, including adjacent lines, overlapping symbols, unclear regions, and similarity between symbols [40]. A study evaluated four classification tools, namely, multi-stage deep neural networks, hidden Markov models, K-nearest neighbors, and support vector machines (SVMs), using both synthetic and original drawing sheet datasets [41]. Although the SVM model demonstrated the best performance, all techniques involved grouping symbols before classification and removing lines for clearer observation. Additionally, a faster R-CNN was employed to detect and classify handwritten symbols in technical diagrams, with a focus on specific document types, such as PFDs and flowcharts, achieving favorable results compared to traditional methods. Various frameworks have been proposed for interpreting engineering sheets and P&ID drawings using deep-learning models. The combination of heuristic-based methods with deep-learning techniques has shown promising results in component detection for engineering drawings. A two-stage process was employed, involving Euclidean metrics to connect pipeline tags and symbols, as well as the probabilistic Hough transform for pipeline detection, to localize symbols and text.