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Reducing human effort in engineering drawing validation
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Text/Graphics Separation in Maps
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Text/Graphics Separation Revisited
inferredcites →https://doi.org/10.1007/978-3-319-65172-9_8openalex-snapshot
Heuristics-Based Detection to Improve Text/Graphics Segmentation in Complex Engineering Drawings
inferredcites →https://doi.org/10.1007/978-3-642-25085-9_1openalex-snapshot
The Dissimilarity Representation for Structural Pattern Recognition
inferredcites →https://doi.org/10.1007/s00521-018-3583-1openalex-snapshot
New trends on digitisation of complex engineering drawings
inferredcites →https://doi.org/10.1016/0031-3203(81)90009-1openalex-snapshot
Generalizing the Hough transform to detect arbitrary shapes
inferredcites →https://doi.org/10.1016/0167-8655(83)90033-8openalex-snapshot
Inexact graph matching for structural pattern recognition
inferredcites →https://doi.org/10.1016/0167-8655(94)00111-fopenalex-snapshot
Interpretation of telephone system manhole drawings
inferredcites →https://doi.org/10.1016/j.compchemeng.2016.04.040openalex-snapshot
Automatic derivation of qualitative plant simulation models from legacy piping and instrumentation diagrams
inferredcites →https://doi.org/10.1016/j.compind.2019.01.005openalex-snapshot
Applying graph matching techniques to enhance reuse of plant design information
inferredcites →https://doi.org/10.1016/j.patcog.2012.01.009openalex-snapshot
Graph embedding in vector spaces by node attribute statistics
inferredcites →https://doi.org/10.1016/j.patcog.2012.07.029openalex-snapshot
Fuzzy multilevel graph embedding
inferredcites →https://doi.org/10.1016/j.patcog.2014.01.002openalex-snapshot
A long trip in the charming world of graphs for Pattern Recognition
inferredcites →https://doi.org/10.1016/j.patrec.2015.08.003openalex-snapshot
Graph Edit Distance: Moving from global to local structure to solve the graph-matching problem
inferredcites →https://doi.org/10.1016/s0031-3203(01)00066-8openalex-snapshot
Graph-based representations and techniques for image processing and image analysis
inferredcites →https://doi.org/10.1016/s0965-9978(98)00022-2openalex-snapshot
Computer interpretation of process and instrumentation drawings
inferredcites →https://doi.org/10.1109/2.144439openalex-snapshot
Celesstin: CAD conversion of mechanical drawings
inferredcites →https://doi.org/10.1109/34.608290openalex-snapshot
A system for recognizing a large class of engineering drawings
inferredcites →https://doi.org/10.1109/coase.2016.7743558openalex-snapshot
Automated identification of components in raster piping and instrumentation diagram with minimal pre-processing
inferredcites →https://doi.org/10.1109/icapr.2009.45openalex-snapshot
Graphic Symbol Recognition Using Auto Associative Neural Network Model
inferredcites →https://doi.org/10.1109/icdarw.2019.60122openalex-snapshot
Digitisation of Assets from the Oil & Gas Industry: Challenges and Opportunities
inferredcites →https://doi.org/10.1109/ijcnn.2018.8489087openalex-snapshot
Symbols Classification in Engineering Drawings
inferredcites →https://doi.org/10.1109/tpami.2004.1265866openalex-snapshot
An eigenspace projection clustering method for inexact graph matching
inferredcites →https://doi.org/10.1109/tsmc.1983.6313167openalex-snapshot
A distance measure between attributed relational graphs for pattern recognition
inferredcites →https://doi.org/10.1142/s0218001404003228openalex-snapshot
THIRTY YEARS OF GRAPH MATCHING IN PATTERN RECOGNITION
inferredcites →https://doi.org/10.1142/s021800141260004xopenalex-snapshot
ON THE GRAPH EDIT DISTANCE COST: PROPERTIES AND APPLICATIONS
inferredcites →https://doi.org/10.1142/s0218001414500013openalex-snapshot
GRAPH MATCHING AND LEARNING IN PATTERN RECOGNITION IN THE LAST 10 YEARS
inferredcites →https://doi.org/10.3390/app8050654openalex-snapshot
Deep Neural Networks for Document Processing of Music Score Images
inferredcites →https://doi.org/10.3390/en12132593openalex-snapshot
A Digitization and Conversion Tool for Imaged Drawings to Intelligent Piping and Instrumentation Diagrams (P&ID)
inferredcites →https://doi.org/10.5220/0007376401630172openalex-snapshot
Automatic Information Extraction from Piping and Instrumentation Diagrams
inferredcites →https://doi.org/10.5281/zenodo.1318390openalex-snapshot
Advances In Deep Learning On Graphs (Gsp'18 Workshop)
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Zero-Error Digitisation and Contextualisation of Piping and Instrumentation Diagrams Using Node Classification and Sub-graph Search
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Towards Automatic Digitalization of Railway Engineering Schematics
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A review of deep learning methods for digitisation of complex documents and engineering diagrams
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Graph neural network-based method for classifying continuous lines in piping and instrumentation diagram
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Tolerance Information Extraction for Mechanical Engineering Drawings – A Digital Image Processing and Deep Learning-based Model
inferred← citeshttps://doi.org/10.1016/j.compind.2021.103442openalex-snapshot
Extraction of dimension requirements from engineering drawings for supporting quality control in production processes
inferred← citeshttps://doi.org/10.1016/j.compind.2021.103463openalex-snapshot
Analyzing content of tasks in Business Process Management. Blending task execution and organization perspectives
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Graph neural network-enabled manufacturing method classification from engineering drawings
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Group of components detection in engineering drawings based on graph matching
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.127532openalex-snapshot
Automatic digitalization of railway interlocking systems engineering drawings based on hybrid machine learning methods
inferred← citeshttps://doi.org/10.1016/j.knosys.2020.106275openalex-snapshot
Interactive online learning for graph matching using active strategies
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Symbols Detection and Classification using Graph Neural Networks
inferred← citeshttps://doi.org/10.1016/j.procir.2023.05.006openalex-snapshot
State of the art applications of engineering graphics and design to enhance innovative product design: a systematic review
inferred← citeshttps://doi.org/10.1038/s41598-025-25506-2openalex-snapshot
Automated inspection of P&ID object recognition using deep learning
inferred← citeshttps://doi.org/10.1080/08839514.2024.2327005openalex-snapshot
Intelligent Extraction of Multi-style and Multi-template Title Block Information Based on Fuzzy Matching
inferred← citeshttps://doi.org/10.1080/10494820.2022.2158477openalex-snapshot
The effect of 3D-stereogram mobile AR on engineering drawing course outcomes among first-year vocational high schoolers with different spatial abilities: a Bloom’s taxonomy perspective
inferred← citeshttps://doi.org/10.1109/ijcnn48605.2020.9207479openalex-snapshot
Pixel-based layer segmentation of complex engineering drawings using convolutional neural networks
inferred← citeshttps://doi.org/10.1109/iraset64571.2025.11008342openalex-snapshot
The impact of augmented reality on the understanding of assembly drawings: case study of a gear train
inferred← citeshttps://doi.org/10.1109/oncon60463.2023.10430907openalex-snapshot
PIDPipe: A Pipeline for Digitizing P&ID with Unsupervised Symbol Localization
inferred← citeshttps://doi.org/10.1109/smc58881.2025.11343056openalex-snapshot
Toward Automated Interdisciplinary Checks in EPC Projects: A Human-AI Collaborative Approach
inferred← citeshttps://doi.org/10.1145/3652620.3688200openalex-snapshot
Ensuring the consistency of information between two versions of a mechanical drawing
inferred← citeshttps://doi.org/10.1186/s40561-023-00289-zopenalex-snapshot
Designing and evaluating an augmented reality system for an engineering drawing course
inferred← citeshttps://doi.org/10.16920/jeet/2021/v34i0/157146openalex-snapshot
An Attempt to Impart Engineering Drawing Standards Through Problem Based Learning Approach
inferred← citeshttps://doi.org/10.3390/app10114005openalex-snapshot
Deep Neural Network for Automatic Image Recognition of Engineering Diagrams
inferred← citeshttps://doi.org/10.3390/app14146106openalex-snapshot
Symbol Detection in Mechanical Engineering Sketches: Experimental Study on Principle Sketches with Synthetic Data Generation and Deep Learning
inferred← citeshttps://doi.org/10.3390/sym12071181openalex-snapshot
Computer-Aided Sketching: Incorporating the Locus to Improve the Three-Dimensional Geometric Design
inferred← citeshttps://doi.org/10.3390/sym18050874openalex-snapshot
Symbol Recognition of Station Signal Layout Drawings Using a Fusion Design of Generalized Focal Loss and Dilated Residual Segmentation
inferred← citeshttps://doi.org/10.48550/arxiv.2108.13912openalex-snapshot
Automatic digital twin data model generation of building energy systems from piping and instrumentation diagrams
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