SDS KoPub VDR: A Benchmark Dataset for Visual Document Retrieval in Korean Public Documents

• URL: http://arxiv.org/abs/2511.04910v2
• Date: Mon, 10 Nov 2025 04:20:56 GMT
• Title: SDS KoPub VDR: A Benchmark Dataset for Visual Document Retrieval in Korean Public Documents
• Authors: Jaehoon Lee, Sohyun Kim, Wanggeun Park, Geon Lee, Seungkyung Kim, Minyoung Lee,
• Abstract summary: Existing benchmarks for visual document retrieval (VDR) largely overlook non-English languages and the structural complexity of official publications.<n>We introduce SDS KoPub VDR, the first large-scale, public benchmark for retrieving and understanding Korean public documents.<n>The benchmark is built upon 361 real-world documents, including 256 files under the KOGL Type 1 license and 105 from official legal portals.
• Score: 10.146296597660598
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Existing benchmarks for visual document retrieval (VDR) largely overlook non-English languages and the structural complexity of official publications. To address this gap, we introduce SDS KoPub VDR, the first large-scale, public benchmark for retrieving and understanding Korean public documents. The benchmark is built upon 361 real-world documents, including 256 files under the KOGL Type 1 license and 105 from official legal portals, capturing complex visual elements like tables, charts, and multi-column layouts. To establish a reliable evaluation set, we constructed 600 query-page-answer triples. These were initially generated using multimodal models (e.g., GPT-4o) and subsequently underwent human verification to ensure factual accuracy and contextual relevance. The queries span six major public domains and are categorized by the reasoning modality required: text-based, visual-based, and cross-modal. We evaluate SDS KoPub VDR on two complementary tasks: (1) text-only retrieval and (2) multimodal retrieval, which leverages visual features alongside text. This dual-task evaluation reveals substantial performance gaps, particularly in multimodal scenarios requiring cross-modal reasoning, even for state-of-the-art models. As a foundational resource, SDS KoPub VDR enables rigorous and fine-grained evaluation and provides a roadmap for advancing multimodal AI in real-world document intelligence. The dataset is available at https://huggingface.co/datasets/SamsungSDS-Research/SDS-KoPub-VDR-Benchmark.

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