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| license: cc0-1.0 |
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| # AncientDoc: A Benchmark for Chinese Ancient Document Understanding |
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| AncientDoc is the first comprehensive benchmark dataset specifically designed for **Chinese Ancient Document Understanding**. It covers multi-task evaluation ranging from **OCR** to **knowledge reasoning**, aiming to promote research on the recognition, understanding, and reasoning capabilities of multimodal large models in the scenario of ancient documents. |
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| ## Dataset Overview |
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| - **Data Scale**: 2,973 pages |
| - **Number of Literatures**: Approximately 100 books |
| - **Types of Literatures**: 14 categories (e.g., collected works, Chu Ci-style poems, poetry and prose criticism, encyclopedic books, catalogs, etc.) |
| - **Time Span**: Spanning from the Warring States period to the Qing Dynasty, covering multiple important historical periods |
| - **Task Types**: |
| 1. **Page-level OCR**: Full-page text recognition (including complex scenarios such as vertical typesetting, variant characters, and annotations) |
| 2. **Vernacular Translation**: Intralingual translation from classical Chinese to modern Chinese |
| 3. **Reasoning-based QA**: Implicit reasoning QA based on the meaning of the text |
| 4. **Knowledge-based QA**: QA based on textual facts and background knowledge |
| 5. **Linguistic Variant QA**: QA related to literary genres, rhetoric, and linguistic styles |
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| ## Data Distribution |
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| ### Distribution by Dynasty |
| - Ming Dynasty: 1,148 pages |
| - Qing Dynasty: 778 pages |
| - Song Dynasty: 540 pages |
| - Tang Dynasty: 208 pages |
| - Han Dynasty: 110 pages |
| - Yuan Dynasty: 69 pages |
| - Southern and Northern Dynasties: 54 pages |
| - Jin Dynasty: 42 pages |
| - Warring States period: 24 pages |
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| ### Distribution by Category (Top 3 by Page Count) |
| 1. Astronomy and Mathematics (238 pages) |
| 2. Confucianism (232 pages) |
| 3. Art (234 pages) |
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| ### Distribution by Script Style |
| - Regular script: Approximately 97% |
| - Cursive script: Approximately 3% |
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| ## Data Format |
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| Data is provided in the form of **images + CSV annotations**: |
