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About this paper
Shu Wei, Nuo Xu
The Paragraph2Graph framework introduces a Graph Neural Network-based approach for document layout analysis, which operates independently of language by relying on visual and spatial features. This method achieves competitive performance on benchmarks, demonstrates cross-language generalization, and provides more precise layout component separation while being computationally efficient.
Key insights
- Leveraging Graph Neural Networks allows for language-independent document analysis by processing visual and spatial features instead of text embeddings.
- A directional graph sampling strategy (connecting to 1-2 nearest neighbors in four directions) reduces computational complexity while preserving crucial spatial relationships.
- Edge classification within the graph framework enables precise separation of layout components, addressing issues like overlapping bounding boxes seen in object detection methods.
Summary by alphaXiv.
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