Benchmarking Vision-Language Models on Chinese Ancient Documents: From OCR to Knowledge Reasoning
On the map: LLM reasoning → Multimodal reasoning
Abstract (arXiv)
Chinese ancient documents, invaluable carriers of millennia of Chinese history and culture, hold rich knowledge across diverse fields but face challenges in digitization and understanding, i.e., traditional methods only scan images, while current Vision-Language Models (VLMs) struggle with their visual and linguistic complexity. Existing document benchmarks focus on English printed texts or simplified Chinese, leaving a gap for evaluating VLMs on ancient Chinese documents. To address this, we present AncientDoc, the first benchmark for Chinese ancient documents, designed to assess VLMs from OCR to knowledge reasoning. AncientDoc includes five tasks (page-level OCR, vernacular translation, reasoning-based QA, knowledge-based QA, linguistic variant QA) and covers 14 document types, over 100 books, and about 3,000 pages. Based on AncientDoc, we evaluate mainstream VLMs using multiple metrics, supplemented by a human-aligned large language model for scoring.
Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Placed in Multimodal reasoning, one of 17 directions in LLM reasoning. Builds on 17 of its 39 references that are in the map. First posted on arXiv 10 Sep 2025.
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12 most cited of 15 All 15 (JSON)
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- 2026HCSU: A Dataset and Benchmark for Fine-Grained Historical Calligraphy Style Understanding5 Jul 2026
- 2026Cross-Temporal Sinhala OCR: Page-Level Adaptation and Diachronic Analysis28 Jun 2026
- 2026Do You Need Text Rectification? Soft Attention Mask Embedding for Rectification-Free Scene Text Spotting18 May 2026
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- 2026Transmittance-Guided Structure-Texture Decomposition for Nighttime Image Dehazing31 Mar 2026
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- 2026Detecting Deepfakes with Multivariate Soft Blending and CLIP-based Image-Text Alignment14 Feb 2026
12 newest of 15 All 15 (JSON)
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