# Benchmarking Vision-Language Models on Chinese Ancient Documents: From OCR to Knowledge Reasoning

arXiv: https://arxiv.org/abs/2509.09731 · HTML: https://mlsanity.com/paper/2509.09731/

## 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.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)
- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 17 of 17)

- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://mlsanity.com/paper/2504.10479/) (2025)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [Qwen2.5-Omni Technical Report](https://mlsanity.com/paper/2503.20215/) (2025)
- [DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding](https://mlsanity.com/paper/2412.10302/) (2024)
- [Seed1.5-VL Technical Report](https://mlsanity.com/paper/2505.07062/) (2025)
- [OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning](https://mlsanity.com/paper/2501.00321/) (2024)
- [Document-Level Machine Translation with Large Language Models](https://mlsanity.com/paper/2304.02210/) (2023)
- [Improving the Transformer Translation Model with Document-Level Context](https://mlsanity.com/paper/1810.03581/) (2018)
- [Scene Text Recognition with Sliding Convolutional Character Models](https://mlsanity.com/paper/1709.01727/) (2017)
- [PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering](https://mlsanity.com/paper/2404.12720/) (2024)

## Built on by (most cited first, 19 of 19)

- [CME-CAD: Heterogeneous Collaborative Multi-Expert Reinforcement Learning for CAD Code Generation](https://mlsanity.com/paper/2512.23333/) (2025)
- [Cognitive Mismatch in Multimodal Large Language Models for Discrete Symbol Understanding](https://mlsanity.com/paper/2603.18472/) (2026)
- [LPCAN: Lightweight Pyramid Cross-Attention Network for Rail Surface Defect Detection Using RGB-D Data](https://mlsanity.com/paper/2601.09118/) (2026)
- [Knowledge-Embedded and Hypernetwork-Guided Few-Shot Substation Meter Defect Image Generation Method](https://mlsanity.com/paper/2601.09238/) (2026)
- [MA-LipNet: Multi-Dimensional Attention Networks for Robust Lipreading](https://mlsanity.com/paper/2601.20881/) (2026)
- [The Diffusion Duet: Harmonizing Dual Channels with Wavelet Suppression for Image Separation](https://mlsanity.com/paper/2602.13361/) (2026)
- [Detecting Deepfakes with Multivariate Soft Blending and CLIP-based Image-Text Alignment](https://mlsanity.com/paper/2602.15903/) (2026)
- [Efficient Image Super-Resolution with Multi-Scale Spatial Adaptive Attention Networks](https://mlsanity.com/paper/2603.00118/) (2026)
- [A Deformable Attention-Based Detection Transformer with Cross-Scale Feature Fusion for Industrial Coil Spring Inspection](https://mlsanity.com/paper/2603.13429/) (2026)
- [Transmittance-Guided Structure-Texture Decomposition for Nighttime Image Dehazing](https://mlsanity.com/paper/2603.29507/) (2026)
- [Hierarchical Awareness Adapters with Hybrid Pyramid Feature Fusion for Dense Depth Prediction](https://mlsanity.com/paper/2604.03339/) (2026)
- [Do You Need Text Rectification? Soft Attention Mask Embedding for Rectification-Free Scene Text Spotting](https://mlsanity.com/paper/2605.18173/) (2026)
- [CulMind: Benchmarking Multimodal Understanding and Reasoning in Chinese Cultural Heritage](https://mlsanity.com/paper/2606.21618/) (2026)
- [Cross-Temporal Sinhala OCR: Page-Level Adaptation and Diachronic Analysis](https://mlsanity.com/paper/2606.29378/) (2026)
- [HCSU: A Dataset and Benchmark for Fine-Grained Historical Calligraphy Style Understanding](https://mlsanity.com/paper/2607.04147/) (2026)
- [SAGE: From Direct Answering to Evidence-Grounded Inference for Chinese Ancient Document Understanding](https://mlsanity.com/paper/2608.24011/) (2026)
- [Rank-Consistent Set Reasoning for Co-Salient Object Detection](https://mlsanity.com/paper/2609.13706/) (2026)
- [Contour-Guided Spectral Routing for Robust Real-Time Pedestrian Detection](https://mlsanity.com/paper/2609.14383/) (2026)
- [LongHisDoc: A Comprehensive Benchmark for Chinese Long Historical Document Understanding](https://mlsanity.com/paper/2610.14531/) (2026)

## 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.