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About this paper
Yihao Ding, Soyeon Caren Han, Jean Lee, Eduard Hovy
This survey provides a systematic review of deep learning frameworks for visually rich document content understanding, categorizing methodologies, analyzing key technical components, and highlighting challenges and emerging trends in multimodal and multi-page document processing. It also compiles and critically examines publicly available benchmark datasets, offering guidance for researchers and practitioners.
Key insights
- Multimodal deep learning models, integrating text, layout, and visual features, consistently outperform text-only approaches in VRD content understanding tasks.
- The field is shifting towards generative models and the integration of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) for enhanced reasoning.
- Large-scale pretraining on diverse document collections is vital for developing robust and generalizable VRD-CU systems.
Summary by alphaXiv.
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