{"type":"ScholarlyArticle","title":"VRD-IU: Lessons from Visually Rich Document Intelligence and Understanding","authors":[{"name":"Yihao Ding","slug":"yihao-ding","given":"Yihao","family":"Ding","affiliation":""},{"name":"Soyeon Caren Han","slug":"soyeon-caren-han","given":"Soyeon Caren","family":"Han","affiliation":""},{"name":"Yan Li","slug":"yan-li","given":"Yan","family":"Li","affiliation":""},{"name":"Josiah Poon","slug":"josiah-poon","given":"Josiah","family":"Poon","affiliation":""}],"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.\r\nThis 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.","doi":"10.24963/ijcai.2025/1258","url":"https://ijcai.org/proceedings/2025/1258","pdf":"https://ijcai.org/proceedings/2025/1258.pdf","markdown":"https://ijcai.org/proceedings/2025/1258.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1258","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Demo Track","track":"Demo Track","number":1258,"pages":{"first":11039,"last":11043},"keywords":["Natural Language Processing: NLP: Applications","Natural Language Processing: NLP: Information extraction"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}