VRD-IU: Lessons from Visually Rich Document Intelligence and Understanding

• URL: http://arxiv.org/abs/2506.01388v1
• Date: Mon, 02 Jun 2025 07:28:28 GMT
• Title: VRD-IU: Lessons from Visually Rich Document Intelligence and Understanding
• Authors: Yihao Ding, Soyeon Caren Han, Yan Li, Josiah Poon,
• Abstract summary: Visually Rich Document Understanding (VRDU) has emerged as a critical field in document intelligence.<n>Form-like documents pose unique challenges due to their complex layouts, multi-stakeholder involvement, and high structural variability.<n>The VRD-IU Competition was introduced, focusing on extracting and localizing key information from multi-format forms.
• Score: 11.588271855615556
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visually Rich Document Understanding (VRDU) has emerged as a critical field in document intelligence, enabling automated extraction of key information from complex documents across domains such as medical, financial, and educational applications. However, form-like documents pose unique challenges due to their complex layouts, multi-stakeholder involvement, and high structural variability. Addressing these issues, the VRD-IU Competition was introduced, focusing on extracting and localizing key information from multi-format forms within the Form-NLU dataset, which includes digital, printed, and handwritten documents. This paper presents insights from the competition, which featured two tracks: Track A, emphasizing entity-based key information retrieval, and Track B, targeting end-to-end key information localization from raw document images. With over 20 participating teams, the competition showcased various state-of-the-art methodologies, including hierarchical decomposition, transformer-based retrieval, multimodal feature fusion, and advanced object detection techniques. The top-performing models set new benchmarks in VRDU, providing valuable insights into document intelligence.

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