Cardinal Graph Convolutional Networks for Document Image Analysis
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Solution Overview
Problem
Graph Convolutional Networks (GCNs) lack directional information, leading to the loss of crucial spatial relationships in document analysis tasks, such as information extraction from invoices, where proximity and spatial arrangements are vital for accurate data extraction.
Innovation Solution
Cardinal Graph Convolutional Networks (CGCNs) are introduced, which encode spatial directions by representing each word as a node in a graph with edges in four cardinal directions, using a features tensor and adjacency tensor to perform convolutions and pooling operations, maintaining permutation invariance and incorporating directional information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Graph Convolutional Networks (GCNs) are used for document analysis, then permutation invariance is maintained, but directional information between nodes is lost
Solution Approach 1:
The patent extends the traditional GCN adjacency matrix to a three-dimensional adjacency tensor that encodes spatial relationships between nodes. By adding a spatial dimension to represent cardinal directions (north, south, east, west), the model maintains permutation invariance while preserving directional information through multi-dimensional tensor operations.
Solution Approach 2:
The patent applies different convolution operations to different spatial directions in the graph structure. By treating each cardinal direction as a distinct local property with its own learnable parameters, the model can capture directional relationships while maintaining overall permutation invariance through the structured tensor framework.
2Productivity
If traditional GCNs are used, then computational efficiency is maintained, but spatial relationship extraction accuracy deteriorates
Solution Approach 1:
The patent segments the graph convolution operation into direction-specific components by using separate adjacency tensors for each cardinal direction. This segmentation allows the model to process spatial relationships in an organized manner, improving extraction accuracy while maintaining computational efficiency through structured tensor operations.
Solution Approach 2:
The patent creates a universal framework that handles both traditional GCN operations and directional spatial relationships through a unified tensor-based approach. The same convolutional framework processes both scalar features and directional information, maintaining computational efficiency while enhancing spatial relationship extraction capability.
Data Source
AI summary
Systems and methods for processing documents based on a cardinal graph convolution network by generating cardinal graph representations representing words as single nodes with edges connected between neighbouring nodes in four cardinal directions. Features tensors are generated for nodes of the cardinal graph representation and the cardinal directions are encoded to generate an adjacency tensor having node neighbour indices. Entries of the adjacency tensor are transformed into a one-hot encoding of the node neighbour indices. Neighbourhood feature tensors are created over node indices and the features in each block may be scaled, convolved and reduced into new feature tensors.