Papers with visually-rich document understanding
Hypergraph based Understanding for Document Semantic Entity Recognition (2024.acl-long)
Copied to clipboard
| Challenge: | Existing document understanding models focus on entity categories while ignoring the extraction of entity boundaries. |
| Approach: | They propose a hypergraph attention document semantic entity recognition framework which uses hypergraph focus to focus on entity boundaries and entity categories at the same time. |
| Outcome: | The proposed framework can improve the performance of existing models on FUNSD, CORD, XFUND and SROIE. |
ReLayout: Towards Real-World Document Understanding via Layout-enhanced Pre-training (2025.coling-main)
Copied to clipboard
| Challenge: | Recent approaches for visually-rich document understanding use manually annotated semantic groups. |
| Approach: | They propose a new variant of the VrDU task that does not use manually annotated semantic groups. |
| Outcome: | The proposed method improves on the existing methods while sacrificing performance. |
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding (2022.findings-emnlp)
Copied to clipboard
Qiming Peng, Yinxu Pan, Wenjin Wang, Bin Luo, Zhenyu Zhang, Zhengjie Huang, Yuhui Cao, Weichong Yin, Yongfeng Chen, Yin Zhang, Shikun Feng, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang
| Challenge: | Existing methods for visually rich document understanding lack layout-centered knowledge . experimental results show that ERNIE-Layout improves layout awareness . |
| Approach: | They propose a document pre-training solution with layout knowledge enhancement in the whole workflow to learn better representations that combine the features from text, layout, and image. |
| Outcome: | The proposed model outperforms existing models on key downstream tasks. |