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GraphRevisedIE: Multimodal information extraction with graph-revised network
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AI
DOI：10.1016/j.patcog.2023.109542.png)
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摘要
En 中文
Key information extraction (KIE) from visually rich documents (VRD) has been a challenging task in document intelligence because of not only the complicated and diverse layouts of VRD that make the model hard to generalize but also the lack of methods to exploit the multimodal features in VRD. In this paper, we propose a light-weight model named GraphRevisedIE that effectively embeds multimodal features such as textual, visual, and layout features from VRD and leverages graph revision and graph convolution to enrich the multimodal embedding with global context. Extensive experiments on multiple real-world datasets show that GraphRevisedIE generalizes to documents of varied layouts and achieves comparable or better performance compared to previous KIE methods. We also publish a business license dataset that contains both real-life and synthesized documents to facilitate research of document KIE. (c) 2023 Elsevier Ltd. All rights reserved.
Keyword:
Document information extraction
Graph convolutional network
Transformer
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Information extraction from historical handwritten document images with a context-aware neural model使用上下文感知神经模型从历史手写文档图像中提取信息.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
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