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Automating this process is an important concern in the process plant industry because presently image P&amp;IDs are manually converted into digital P&amp;IDs. The proposed method comprises object recognition within the P&amp;ID images, topology reconstruction of recognized objects, and digital P&amp;ID generation. A data set comprising 75\u2009031 symbol, 10\u2009073 text, and 90\u2009054 line data was constructed to train the deep neural networks used for recognizing symbols, text, and lines. Topology reconstruction and digital P&amp;ID generation were developed based on traditional rule-based approaches. Five test P&amp;IDs were digitalized in the experiments. The experimental results for recognizing symbols, text, and lines showed good precision and recall performance, with averages of 96.65%\/96.40%, 90.65%\/92.16%, and 95.25%\/87.91%, respectively. The topology reconstruction results showed an average precision of 99.56% and recall of 96.07%. The digitization was completed in &amp;lt;3.5 hours (8488.2 s on average) for five test P&amp;IDs.<\/jats:p>","DOI":"10.1093\/jcde\/qwac056","type":"journal-article","created":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T11:42:49Z","timestamp":1655725369000},"page":"1298-1326","source":"Crossref","is-referenced-by-count":33,"title":["End-to-end digitization of image format piping and instrumentation diagrams at an industrially applicable level"],"prefix":"10.1093","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7073-5318","authenticated-orcid":false,"given":"Byung Chul","family":"Kim","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Korea University of Technology and Education , Cheonan 31253, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyungki","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Computer Science and Engineering, Jeonbuk National University , Jeonju 54896, 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