Applied Artificial Intelligence (AI) in engineering is gaining significant traction. AI object detection methods can be applied in the engineering industry to extract information from engineering drawings, offering immense benefits to engineers. A promising application of AI in industrial engineering is symbol recognition applied to engineering drawings. However, these drawings often exhibit areas with a high density of symbols, as well as noise in the form of markups, indicating revisions. These factors could cause symbol misclassification or omission, impacting applications reliant on accurate symbol recognition. This study evaluates the accuracy of a symbol recognition model on engineering drawings called Piping and Instrumen-tation Diagrams (P&IDs) exhibiting varying levels of density and markups causing noise. Despite the assumption that density poses a challenge for accurate symbol recognition in engineering drawings, our study reveals that density has no significant impact on recognition performance when a dense detector is employed. In addition, we quantitatively show that markup-induced noise on engineering drawings negatively influences recognition accuracy. Finally, we provide recommendations regarding the applicability of symbol recognition in engineering applications. The study's findings and recommendations apply to any P&IDs, regardless of the standard used, as they were evaluated on various worldwide projects. Moreover, the research not only contributes to the advancement of symbol recognition on P&IDs, but also can be applied to other types of engineering drawings. Thus, it holds the potential for enhancing symbol recognition in various real-world industrial applications and research.