Visually Rich Document

Visually rich documents (VRDs), containing diverse elements like text, images, tables, and charts, present a significant challenge for automated information extraction. Current research focuses on developing robust multimodal models, often leveraging transformer architectures and graph neural networks, to effectively integrate visual and textual information, addressing issues like layout understanding and reading order prediction to improve information extraction accuracy and efficiency. This field is crucial for advancing document understanding across various domains, impacting applications ranging from scientific literature analysis to business process automation.