Papers with Doc-GCN
Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysis (2022.coling-1)
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| Challenge: | Document Layout Analysis tasks rely on visual cues to understand documents . traditional deep learning-based methods fail to recognize the layout and components of unstructured documents based on the document structure and the boundaries of each layout region. |
| Approach: | They propose a way to harmonize and integrate heterogeneous aspects for Document Layout Analysis by using graph convolutional networks to enhance each aspect of features. |
| Outcome: | The proposed task is based on three widely used datasets: PubLayNet, FUNSD, and DocBank. |