[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-664808-105":59,"doc-detail-664808-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","new-trends-on-digitisation-of-complex-engineering-drawings","New trends on digitisation of complex engineering drawings","","Engineering drawings serve as key representations of circuits, devices, processes, and facilities across industries such as oil and gas and mechanical engineering. Digitising these legacy documents is increasingly important for extracting usable information, but automatic analysis remains incomplete despite advances in image processing and deep neural networks. The paper proposes a general framework for complex engineering drawing digitisation, reviews relevant literature, and discusses contextualisation for piping and instrumentation diagrams. It also examines how deep learning in machine vision can be applied and outlines future research directions.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/new-trends-on-digitisation-of-complex-engineering-drawings/664808/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/new-trends-on-digitisation-of-complex-engineering-drawings/664808.png","ImageObject",300,407,{"name":92,"@type":93},"Fans","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-29",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is digitisation of engineering drawings important?","Question",{"text":112,"@type":113},"Digitising engineering drawings enables legacy diagrams and documents to provide richer information for industry decision-making and future practices, especially as diagram volumes grow.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What challenges remain for automatic analysis of digitised engineering drawings?",{"text":117,"@type":113},"Automatic analysis is still far from complete because it often requires robust symbol detection, classification, and contextualisation, even though image processing advances such as deep neural networks have progressed.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the paper propose to digitise complex engineering drawings like P&IDs?",{"text":121,"@type":113},"It presents a general digitisation framework and discusses contextualising digitised information for specific application rule sets, focusing on piping and instrumentation diagrams and how deep learning can support machine vision tasks.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},664808,1793273581,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144},5909892330395,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Neural Computing and Applications  \n[https://doi.org/10.1007/s00521-018-3583-1](https://doi.org/10.1007/s00521-018-3583-1)  \nS. I. : EANN 2017  \nNew trends on digitisation of complex engineering drawings  \nCarlos Francisco Moreno-Garca1  • Eyad Elyan 1 • Chrisina Jayne2  \nReceived: 13 November 2017/Accepted: 4 June 2018  \n􀀂 The Author(s) 2018  \nAbstract  \nEngineering drawings are commonly used across different industries such as oil and gas, mechanical engineering and others. Digitising these drawings is becoming increasingly important. This is mainly due to the legacy of drawings and documents that may provide rich source of information for industries. Analysing these drawings often requires applying a set of digital image processing methods to detect and classify symbols and other components. Despite the recent significant advances in image processing, and in particular in deep neural networks, automatic analysis and processing of these engineering drawings is still far from being complete. This paper presents a general framework for complex engineering drawing digitisation. A thorough and critical review of relevant literature, methods and algorithms in machine learning and machine vision is presented. Real-life industrial scenario on how to contextualise the digitised information from specifictype of these drawings, namely piping and instrumentation diagrams, is discussed in details. A discussion of how new trends on machine vision such as deep learning could be applied to this domain is presented with conclusions and suggestions for future research directions.  \nKeywords Engineering drawing 􀀂 Digitisation 􀀂 Contextualisation 􀀂 Segmentation 􀀂 Feature extraction 􀀂 Recognition 􀀂 Classification 􀀂 Deep learning 􀀂 Convolutional neural networks  \n1 Introduction  \nAn engineering drawing (ED) is a schematic representation which depicts the flow or constitution of a circuit, device, process or facility. Some examples of EDs include logical gate circuits, mechanical or architectural drawings. Thereis an increasing demand in different industries for developing digitisation frameworks for processing and analysing these diagrams. Having such framework will provide a unique opportunity for relevant industries to make use of large volumes of diagrams in informing their decisionmaking process and future practices.  \n& Carlos Francisco Moreno-Garc´ıa[c.moreno-garcia@rgu.ac.uk](c.moreno-garcia@rgu.ac.uk)  \nEyad Elyan  \n[eyad.elyan@rgu.ac.uk](eyad.elyan@rgu.ac.uk)  \nChrisina Jayne  \n[cjayne@brookes.ac.uk](cjayne@brookes.ac.uk)  \n1 The Robert Gordon University, Garthdee Road, Aberdeen, UK  \n2 Oxford Brookes University, Wheatley Campus, Oxford, UK  \nDigitising EDs require applying digital image processing techniques through a sequence of steps including preprocessing, symbol detection, classification and some times require inferring the relations between symbols within the drawings (contextualisation) . Several review papers that discuss digitising these drawings or similar type of documents is available in the literature. Some review papers were mainly dedicated to the domain of the documents or engineering drawings. These include review papers on analysing musical notes [13], conversion of paper-based mechanical drawings into CAD files for 3D reconstruction [64, 109], and optical character recognition (OCR)[70, 78], and [88] . Other reviews focused on specific components of the digitisation process, such as symbols detection [25, 28], symbols representation [133], and symbols classification [1, 76] .  \nMotivated by a partnership between academia and the Oil & Gas industry, a subset of EDs called complex EDs has been identified in practice [87] . Some examples are chemical process diagrams, complex circuit drawings, process flow diagrams (PFDs), sensor diagrams (SDs) and piping and instrumentation diagrams (P&IDs) . An example of the latter is shown in Fig. 1. For this type of drawings,  \n1 3  \nFig. 1 Example of a process and instrumentation diagram (P","cbCaisOosZ4VeG55","https://ap.wps.com/l/cbCaisOosZ4VeG55","pdf",1602291,18,"English","# Abstract\n# 1 Introduction\n## Engineering drawing digitisation need\n## Digital image processing pipeline and contextualisation\n## Related review efforts and complex engineering drawings","[{\"question\":\"Why is digitisation of engineering drawings important?\",\"answer\":\"Digitising engineering drawings enables legacy diagrams and documents to provide richer information for industry decision-making and future practices, especially as diagram volumes grow.\"},{\"question\":\"What challenges remain for automatic analysis of digitised engineering drawings?\",\"answer\":\"Automatic analysis is still far from complete because it often requires robust symbol detection, classification, and contextualisation, even though image processing advances such as deep neural networks have progressed.\"},{\"question\":\"How does the paper propose to digitise complex engineering drawings like P\\u0026IDs?\",\"answer\":\"It presents a general digitisation framework and discusses contextualising digitised information for specific application rule sets, focusing on piping and instrumentation diagrams and how deep learning can support machine vision tasks.\"}]","New trends on digitisation of complex engineering drawings | PDF",45]