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Symbols classification in engineering drawings.

Elyan, Eyad; Garcia, Carlos Moreno; Jayne, Chrisina


Chrisina Jayne


Technical drawings are commonly used across different industries such as Oil and Gas, construction, mechanical and other types of engineering. In recent years, the digitization of these drawings is becoming increasingly important. In this paper, we present a semi-automatic and heuristic-based approach to detect and localise symbols within these drawings. This includes generating a labeled dataset from real world engineering drawings and investigating the classification performance of three different state-of the art supervised machine learning algorithms. In order to improve the classification accuracy the dataset was pre-processed using unsupervised learning algorithms to identify hidden patterns within classes. Testing and evaluating the proposed methods on a dataset of symbols representing one standard of drawings, namely Process and Instrumentation (P&ID) showed very competitive results.


ELYAN, E., MORENO GARCIA, C. and JAYNE, C. 2018. Symbols classification in engineering drawings. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489087. Available from:

Conference Name 2018 International joint conference on neural networks (IJCNN 2018)
Conference Location Rio de Janeiro, Brazil
Start Date Jul 8, 2018
End Date Jul 13, 2018
Acceptance Date Mar 15, 2018
Online Publication Date Jul 8, 2018
Publication Date Dec 31, 2018
Deposit Date Apr 17, 2018
Publicly Available Date Jul 8, 2018
Print ISSN 2161-4393
Electronic ISSN 2161-4407
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Article Number 8489087
Series ISSN 2161-4407
Keywords Technical drawings; Digitisation; Symbols; Engineering
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