Layout-grounded iterative refinement for auditable organization–task extraction in Vietnamese administrative documents
Year: 2026
Type: article
Abstract: Automatic processing of Vietnamese administrative documents is challenging owing to factors like compact layout, lengthy action clause, and organization list that involve many coordinators. In this paper, we introduce a five-step extraction pipeline using layout for auditing organization-task pairing in scanned administrative documents. Our pipeline involves layout-preserving OCR, contextual grouping, faithful grounding of candidates, task refinement guided by critic, and finally verification of organization. Rather than treating the task as end-to-end multimodal reasoning, the proposed pipeline uses visual and layout information mainly for layout-preserving OCR and then constrains downstream extraction to source-grounded textual evidence. A benchmark of 1,409 documents containing 18,359 pairs of annotations between organizations and tasks is constructed for evaluation purposes. With the primary strict protocol, the whole process yields 94.1% precision, 93.5% recall, and 93.8% F1 scores; under the relaxed protocol, it yields 96.1% F1 score. In order to quantify the benefit over simply prompting, the evaluation setup also contains the controlled baselines of OCR-text pairs using simple chunking, only grouping context formation, and retrieval-based context formation, in addition to the document encoder baseline accounting for the layout information. The grounding-related comparison shows that explicit evidence-window construction alone yields only a modest gain over grouped one-shot extraction, whereas the staged ablation demonstrates that the most substantial benefit is obtained once grounded evidence is coupled with critic-assisted task refinement; the last organization-validation stage handles collaborator-heavy cases not solvable at the first extraction stage.
Source: Discover Artificial Intelligence
Authors: Vu Nguyen Hong, Hung To Phuoc
Institution: Ho Chi Minh City University of Industry and Trade
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,590
OpenAlex ID: W7162886972