doc2graph
Doc2Graph transforms documents into graphs and exploit a GNN to solve several tasks.
Discover how Doc2Graph leverages geometric edge features and edge classification for effective layout analysis and table detection in scanned documents. Learn the technical details.
How to Debug Issues with Graph Construction from PDF Documents in doc2graphDebug doc2graph PDF issues. Learn how to fix unimplemented methods causing errors during graph construction from PDF files and prevent crashes.
How the knn_connection Algorithm Finds Neighboring Bounding Boxes in doc2graphLearn how the knn_connection algorithm in doc2graph builds a spatial kNN graph using projection maps, expanding search windows, and Euclidean distance to find neighboring bounding boxes.
How to Create a Custom Model Configuration in doc2graph by Modifying YAML FilesLearn to create a custom model configuration in doc2graph by modifying YAML files. Adjust hyperparameters and load your custom settings easily for enhanced training.
DGL RPC Vulnerabilities: Security Considerations for the doc2graph RepositorySecure your doc2graph projects by understanding DGL RPC vulnerabilities. Learn how this repository mitigates risks by restricting Deep Graph Library operations locally, preventing unauthorized remote access.
How Doc2Graph Saves Inference Results as JSON and Visualization ImagesDiscover how Doc2Graph saves inference results as JSON and PNG images. Explore extracted key-value pairs and visualized relationships stored in the inference folder.
Understanding the --add-eweights Flag in doc2graph: Enabling Geometric Edge Features and WeightsUnlock geometric edge features and weights with the --add-eweights flag in doc2graph. Enhance GNNs by capturing spatial relationships with polar coordinates. Learn more!
How to Switch Between FUNSD and PAU Datasets for Training in Doc2GraphEasily switch between FUNSD and PAU datasets for Doc2Graph training using the `src-data` argument in main.py. Select your preferred dataset and optimize your graph generation.
How Doc2Graph Uses EasyOCR for Text Extraction in Custom InferenceDiscover how Doc2Graph uses EasyOCR to extract text for custom inference, converting detected text and polygons into node features for graph neural networks.
Message Passing Architecture in GcnSAGELayer: A Deep Dive into Doc2Graph's Graph ConvolutionExplore the message passing architecture in GcnSAGELayer. Doc2Graph merges GCN weighted aggregation and GraphSAGE concatenation for advanced graph convolutions. Learn more.
How InputProjector Handles Multi-Modal Feature Fusion in Doc2GraphLearn how InputProjector achieves multi-modal feature fusion by projecting and concatenating diverse document slices into a unified vector for GNN processing.
NodeClassifier, EdgeClassifier, and E2E Models in doc2graph: Architecture Differences ExplainedDiscover the differences between NodeClassifier, EdgeClassifier, and E2E models in doc2graph. Learn how each architecture handles node and edge classification tasks for efficient graph analysis.
Have a question about this repo?
These articles cover the highlights, but your codebase questions are specific. Give your agent direct access to the source. Share this with your agent to get started:
curl -s "https://instagit.com/install.md" 
Maintain an open-source project? Get it listed too →