Graph Convolution for Multimodal Information Extraction from Visually Rich Documents
This addresses the challenge of document understanding for business and daily life applications where visual and layout information is critical, representing an incremental improvement over existing methods.
The paper tackles the problem of extracting information from visually rich documents (VRDs) by introducing a graph convolution-based model that combines textual and visual features, outperforming BiLSTM-CRF baselines by significant margins on two real-world datasets.
Visually rich documents (VRDs) are ubiquitous in daily business and life. Examples are purchase receipts, insurance policy documents, custom declaration forms and so on. In VRDs, visual and layout information is critical for document understanding, and texts in such documents cannot be serialized into the one-dimensional sequence without losing information. Classic information extraction models such as BiLSTM-CRF typically operate on text sequences and do not incorporate visual features. In this paper, we introduce a graph convolution based model to combine textual and visual information presented in VRDs. Graph embeddings are trained to summarize the context of a text segment in the document, and further combined with text embeddings for entity extraction. Extensive experiments have been conducted to show that our method outperforms BiLSTM-CRF baselines by significant margins, on two real-world datasets. Additionally, ablation studies are also performed to evaluate the effectiveness of each component of our model.