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Graph neural network-enabled manufacturing method classification from engineering drawings
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1006/cviu.1997.0585openalex-snapshot
Segmentation and Recognition of Dimensioning Text from Engineering Drawings
inferredcites →https://doi.org/10.1007/3-540-64381-8_54openalex-snapshot
Analysis of engineering drawings: State of the art and challenges
inferredcites →https://doi.org/10.1007/978-1-4419-8167-7openalex-snapshot
Analysis of Engineering Drawings and Raster Map Images
inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot
U-Net: Convolutional Networks for Biomedical Image Segmentation
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(88)90017-9openalex-snapshot
Handprinted symbol recognition system
inferredcites →https://doi.org/10.1016/j.compind.2020.103198openalex-snapshot
Reducing human effort in engineering drawing validation
inferredcites →https://doi.org/10.1016/j.neunet.2020.05.025openalex-snapshot
Deep learning for symbols detection and classification in engineering drawings
inferredcites →https://doi.org/10.1016/s0007-8506(07)62741-3openalex-snapshot
Automatic Scanning and Interpretation of Engineering Drawings for CAD-Processes
inferredcites →https://doi.org/10.1109/cvpr.2018.00474openalex-snapshot
MobileNetV2: Inverted Residuals and Linear Bottlenecks
inferredcites →https://doi.org/10.1109/cvprw50498.2020.00096openalex-snapshot
Automatic Digitization of Engineering Diagrams using Deep Learning and Graph Search
inferredcites →https://doi.org/10.1109/cvprw50498.2020.00294openalex-snapshot
CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents
inferredcites →https://doi.org/10.1109/fskd.2008.184openalex-snapshot
Knowledge Extraction from Structured Engineering Drawings
inferredcites →https://doi.org/10.1134/s0361768820010041openalex-snapshot
Clustering Algorithm for a Set of Machine Parts on the Basis of Engineering Drawings
inferredcites →https://doi.org/10.1142/9789812812483_0020openalex-snapshot
Automatic Understanding of Technical Drawings: Symbol Extraction by Geometric and Morphological Methods
inferredcites →https://doi.org/10.1145/3202661openalex-snapshot
Vectorization of Line Drawings via Polyvector Fields
inferredcites →https://doi.org/10.1145/3326362openalex-snapshot
Dynamic Graph CNN for Learning on Point Clouds
inferredcites →https://doi.org/10.1145/357994.358023openalex-snapshot
A fast parallel algorithm for thinning digital patterns
inferredcites →https://doi.org/10.1145/62065.62074openalex-snapshot
Parallel thinning with two-subiteration algorithms
inferredcites →https://doi.org/10.1504/ijcat.2005.006467openalex-snapshot
Content-based retrieval of technical drawings
inferredcites →https://doi.org/10.3138/fm57-6770-u75u-7727openalex-snapshot
ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE
inferredcites →https://doi.org/10.48550/arxiv.physics/0004057openalex-snapshot
The information bottleneck method
inferredcites →https://doi.org/10.5220/0007376401630172openalex-snapshot
Automatic Information Extraction from Piping and Instrumentation Diagrams
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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A Directional Graph Framework to Integrate Engineering Data with Application to Chemical Process
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From Paper to Pixels: A Multi-modal Approach to Understand and Digitize Assembly Drawings for Automated Systems
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Object Detection in Engineering Drawings: An End-to-End Conversion Pipeline from 2D Drafting Annotations to 3D Product Manufacturing Information
inferred← citeshttps://doi.org/10.1007/s10462-024-10779-2openalex-snapshot
A review of deep learning methods for digitisation of complex documents and engineering diagrams
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Component segmentation of engineering drawings using Graph Convolutional Networks
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Multi-view expressive graph neural networks for 3D CAD model classification
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Automated MEP Drawing Analysis Using Computer Vision Preprocessing and Vision Language Models
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Dimensional data extraction from engineering drawings
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A novel complex-high-order graph convolutional network paradigm: ChyGCN
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An Approach to Engineering Drawing Organization: Title Block Detection and Processing
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AI-Driven Recognition of Component Relationships in Engineering Drawings: Current Status, Challenges, and Future Directions
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ChatCAD: An MLLM-Guided Framework for Zero-shot CAD Drawing Restoration
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Fine-Scale Feature Encoding in Geometric Pretraining for Engineering Surrogate Modeling
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