A Cross-Temporal Perception Benchmark for the Evolutionary Trajectory of Chinese Characters
δΈζη β’ Paper β’ GitHub β’ HuggingFace β’ ModelScope
Chronicles-OCR is the first comprehensive benchmark specifically designed to evaluate the cross-temporal visual perception capabilities of VLLMs across the complete evolutionary trajectory of Chinese characters β the "Seven Chinese Scripts".
Curated in collaboration with top-tier institutional domain experts (the Key Laboratory of Oracle Bone Inscription Information Processing at Anyang Normal University and the Palace Museum), the dataset comprises 2,800 strictly balanced images encompassing highly diverse physical media, ranging from tortoise shells to paper-based calligraphy.
The "Seven Chinese Scripts" (ζ±εδΈδ½) refer to the seven canonical script forms that emerged throughout the evolution of Chinese characters over more than 5,000 years:
Among these, the first five (Oracle Bone β Regular) successively served as formal writing systems in their respective eras, while Cursive and Running scripts developed primarily as auxiliary styles for informal and rapid writing.
| Item | Details |
|---|---|
| Total Images | 2,800 (400 per script Γ 7 scripts) |
| Script Coverage | All Seven Chinese Scripts |
| Annotation | Stage-Adaptive: character-level for archaic, paragraph-level for mature scripts |
| Expert Partners | Anyang Normal University (Oracle Bone), Palace Museum (ClericalβCursive) |
| Tasks | 4 evaluation tasks |
| Task | Short Name | Scope | Metric |
|---|---|---|---|
| Cross-period Character Spotting | Spotting | Oracle Bone, Bronze, Seal | F1 @ IoU > 0.75 |
| Fine-grained Archaic Character Recognition | Recognition | Oracle Bone, Bronze, Seal | Exact-match Accuracy |
| Ancient Text Parsing | Parsing | All Seven Scripts | 1 β NED (Levenshtein) |
| Script Classification | Classification | All Seven Scripts | Accuracy |
| Model | Think | Avg Spot. | Avg Fine. | Avg Pars. | Avg Class. | OB Spot. | OB Fine. | OB Pars. | OB Class. | Br Spot. | Br Fine. | Br Pars. | Br Class. | Se Spot. | Se Fine. | Se Pars. | Se Class. |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Open-Source Models | |||||||||||||||||
| InternVL3.5-8B | 0.1 | 6.0 | 0.07 | 56.7 | 0.0 | 1.1 | 0.01 | 86.2 | 0.0 | 2.2 | 0.03 | 7.0 | 0.2 | 14.5 | 0.17 | 77.0 | |
| InternVL3.5-A28B | 0.5 | 15.7 | 0.13 | 79.0 | 0.0 | 2.5 | 0.02 | 96.3 | 0.4 | 7.8 | 0.08 | 79.2 | 1.0 | 36.8 | 0.29 | 61.5 | |
| Qwen2.5-VL-7B | 0.0 | 7.4 | 0.07 | 71.8 | 0.0 | 4.0 | 0.02 | 93.8 | 0.0 | 4.5 | 0.04 | 22.5 | 0.0 | 13.8 | 0.14 | 99.2 | |
| Qwen2.5-VL-72B | 0.0 | 0.0 | 0.07 | 74.2 | 0.0 | 0.0 | 0.01 | 98.0 | 0.0 | 0.0 | 0.04 | 26.0 | 0.0 | 0.0 | 0.16 | 98.5 | |
| Qwen3-VL-2B | 2.1 | 10.7 | 0.12 | 73.0 | 0.0 | 1.4 | 0.00 | 96.6 | 0.8 | 6.8 | 0.06 | 36.5 | 5.7 | 24.0 | 0.31 | 85.8 | |
| Qwen3-VL-8B | 3.4 | 17.3 | 0.18 | 73.7 | 0.2 | 3.4 | 0.01 | 98.6 | 2.5 | 11.0 | 0.10 | 24.0 | 7.5 | 37.5 | 0.42 | 98.5 | |
| Qwen3-VL-8B | β | 1.0 | 9.1 | 0.09 | 67.3 | 0.0 | 3.7 | 0.03 | 97.7 | 0.2 | 7.0 | 0.05 | 31.8 | 2.8 | 16.8 | 0.20 | 72.5 |
| Qwen3-VL-A22B | 7.8 | 17.5 | 0.19 | 91.8 | 0.3 | 5.4 | 0.01 | 99.2 | 6.5 | 12.2 | 0.12 | 80.2 | 16.6 | 35.0 | 0.43 | 96.0 | |
| Qwen3-VL-A22B | β | 2.1 | 13.6 | 0.17 | 87.3 | 0.1 | 4.2 | 0.03 | 98.0 | 0.9 | 10.2 | 0.11 | 66.8 | 5.3 | 26.2 | 0.37 | 97.2 |
| Qwen3.5-A3B | 5.6 | 16.2 | 0.20 | 76.5 | 0.2 | 5.1 | 0.02 | 99.7 | 5.3 | 11.5 | 0.12 | 30.0 | 11.2 | 32.0 | 0.45 | 99.8 | |
| Qwen3.5-A17B | 9.7 | 22.6 | 0.22 | 88.3 | 0.5 | 9.1 | 0.02 | 99.7 | 9.2 | 17.5 | 0.13 | 67.2 | 19.4 | 41.3 | 0.50 | 98.0 | |
| Gemma 4 31B it | 2.3 | 7.0 | 0.04 | 70.0 | 0.0 | 3.1 | 0.01 | 72.6 | 1.0 | 6.5 | 0.03 | 74.8 | 6.0 | 11.2 | 0.10 | 62.7 | |
| MiniCPM-V 4.5 | β | 0.0 | 4.8 | 0.02 | 73.8 | 0.0 | 2.5 | 0.01 | 95.2 | 0.0 | 5.5 | 0.03 | 18.0 | 0.1 | 9.0 | 0.04 | 82.5 |
| Molmo 7B-D 0924 | 0.0 | 0.1 | 0.00 | 24.2 | 0.0 | 0.0 | 0.01 | 40.8 | 0.0 | 0.2 | 0.00 | 0.0 | 0.0 | 0.0 | 0.00 | 20.5 | |
| Molmo 72B 0924 | 0.0 | 0.3 | 0.00 | 34.7 | 0.0 | 0.5 | 0.00 | 28.0 | 0.0 | 0.5 | 0.00 | 0.8 | 0.0 | 0.0 | 0.00 | 82.0 | |
| Ovis2.6-30B-A3B | β | 1.9 | 9.0 | 0.09 | 68.3 | 0.1 | 2.0 | 0.01 | 89.8 | 0.7 | 7.5 | 0.06 | 13.5 | 6.8 | 24.5 | 0.25 | 79.0 |
| GLM-4.5V 108B | β | 1.4 | 6.1 | 0.05 | 76.8 | 0.1 | 4.2 | 0.03 | 100 | 2.0 | 6.5 | 0.05 | 15.5 | 3.3 | 9.2 | 0.10 | 91.5 |
| Kimi K2.5 | 5.0 | 27.1 | 0.22 | 96.4 | 0.1 | 11.5 | 0.05 | 100 | 7.5 | 25.8 | 0.19 | 90.0 | 12.5 | 58.5 | 0.60 | 95.5 | |
| Kimi K2.5 | β | 1.8 | 20.3 | 0.22 | 94.7 | 0.0 | 10.2 | 0.05 | 99.8 | 1.2 | 17.5 | 0.20 | 85.8 | 6.0 | 44.8 | 0.57 | 93.5 |
| Proprietary Models | |||||||||||||||||
| GPT-4o | 0.1 | 1.5 | 0.02 | 82.0 | 0.0 | 0.5 | 0.01 | 96.5 | 0.0 | 1.0 | 0.02 | 46.8 | 0.3 | 4.5 | 0.06 | 89.0 | |
| GPT-5 | 0.4 | 3.7 | 0.04 | 88.1 | 0.0 | 4.0 | 0.00 | 98.2 | 0.0 | 4.0 | 0.04 | 60.5 | 1.6 | 4.5 | 0.12 | 97.5 | |
| Seed 1.8 | 9.2 | 20.6 | 0.16 | 94.7 | 0.4 | 9.2 | 0.03 | 99.5 | 9.4 | 15.8 | 0.17 | 80.5 | 26.7 | 45.0 | 0.42 | 99.0 | |
| Seed 1.8 | β | 7.4 | 17.1 | 0.17 | 96.7 | 0.4 | 8.8 | 0.04 | 99.5 | 5.8 | 14.8 | 0.18 | 90.0 | 23.3 | 36.2 | 0.43 | 97.5 |
| Seed 2.0 Pro | 16.5 | 24.5 | 0.18 | 95.9 | 3.0 | 11.0 | 0.03 | 99.5 | 19.9 | 30.8 | 0.22 | 92.2 | 40.7 | 41.5 | 0.43 | 93.8 | |
| Seed 2.0 Pro | β | 15.3 | 23.3 | 0.21 | 96.6 | 2.4 | 11.2 | 0.04 | 99.8 | 17.8 | 26.0 | 0.26 | 92.2 | 39.1 | 37.5 | 0.49 | 94.5 |
| MiMo-V2-Omni | β | 0.4 | 8.6 | 0.08 | 87.7 | 0.0 | 6.5 | 0.04 | 99.5 | 0.2 | 8.0 | 0.07 | 58.5 | 1.5 | 9.8 | 0.15 | 93.0 |
| Gemini 2.5 Pro | β | 0.8 | 7.5 | 0.07 | 87.5 | 0.0 | 5.8 | 0.04 | 99.5 | 0.2 | 7.0 | 0.06 | 80.5 | 2.8 | 10.8 | 0.14 | 70.2 |
| Gemini 3.1 Pro | β | 2.6 | 19.5 | 0.15 | 93.8 | 0.0 | 14.0 | 0.05 | 99.5 | 2.5 | 22.5 | 0.18 | 84.5 | 7.8 | 32.2 | 0.32 | 93.2 |
| Claude Opus 4.7 | β | 0.4 | 10.0 | 0.08 | 90.4 | 0.0 | 4.8 | 0.03 | 93.8 | 0.1 | 9.5 | 0.05 | 80.5 | 1.4 | 21.5 | 0.21 | 93.8 |
OB = Oracle Bone, Br = Bronze, Se = Seal. Bold = best, scores are H-mean (Spot.), Accuracy (Fine./Class.), NED (Pars.).
| Model | Think | Avg Pars. | Avg Class. | Cl Pars. | Cl Class. | Re Pars. | Re Class. | Ru Pars. | Ru Class. | Cu Pars. | Cu Class. |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Open-Source Models | |||||||||||
| InternVL3.5-8B | 0.40 | 35.6 | 0.41 | 1.8 | 0.51 | 69.4 | 0.38 | 52.9 | 0.30 | 35.0 | |
| InternVL3.5-A28B | 0.56 | 58.1 | 0.54 | 28.5 | 0.69 | 85.5 | 0.56 | 63.3 | 0.46 | 75.2 | |
| Qwen2.5-VL-7B | 0.44 | 34.8 | 0.54 | 8.0 | 0.62 | 17.0 | 0.42 | 36.4 | 0.21 | 90.5 | |
| Qwen2.5-VL-72B | 0.49 | 57.2 | 0.59 | 18.0 | 0.66 | 91.5 | 0.46 | 56.6 | 0.26 | 86.0 | |
| Qwen3-VL-2B | 0.57 | 35.2 | 0.61 | 5.5 | 0.71 | 11.8 | 0.50 | 37.9 | 0.42 | 93.0 | |
| Qwen3-VL-8B | 0.66 | 60.9 | 0.69 | 32.5 | 0.77 | 97.2 | 0.64 | 59.1 | 0.56 | 81.0 | |
| Qwen3-VL-8B | β | 0.49 | 45.9 | 0.52 | 11.2 | 0.64 | 79.7 | 0.51 | 53.4 | 0.32 | 56.2 |
| Qwen3-VL-A22B | 0.66 | 64.9 | 0.69 | 36.5 | 0.73 | 95.5 | 0.66 | 68.3 | 0.59 | 82.0 | |
| Qwen3-VL-A22B | β | 0.65 | 60.4 | 0.67 | 31.0 | 0.75 | 93.5 | 0.65 | 62.3 | 0.54 | 78.0 |
| Qwen3.5-A3B | 0.71 | 68.1 | 0.79 | 36.8 | 0.81 | 84.2 | 0.68 | 75.6 | 0.57 | 84.2 | |
| Qwen3.5-A17B | 0.73 | 72.2 | 0.81 | 52.0 | 0.81 | 81.3 | 0.67 | 75.3 | 0.66 | 89.4 | |
| Gemma 4 31B it | 0.34 | 57.1 | 0.37 | 9.6 | 0.56 | 81.9 | 0.33 | 65.0 | 0.09 | 84.5 | |
| MiniCPM-V 4.5 | β | 0.40 | 44.9 | 0.45 | 2.8 | 0.61 | 87.5 | 0.38 | 56.9 | 0.15 | 48.8 |
| Molmo 7B-D 0924 | 0.01 | 16.9 | 0.01 | 70.8 | 0.01 | 3.0 | 0.01 | 0.7 | 0.01 | 0.5 | |
| Molmo 72B 0924 | 0.00 | 9.1 | 0.00 | 6.8 | 0.01 | 16.5 | 0.01 | 3.2 | 0.00 | 12.8 | |
| Ovis2.6-30B-A3B | β | 0.53 | 39.7 | 0.54 | 8.5 | 0.63 | 77.9 | 0.57 | 71.6 | 0.42 | 12.2 |
| GLM-4.5V 108B | β | 0.44 | 56.6 | 0.45 | 11.5 | 0.61 | 84.5 | 0.44 | 63.3 | 0.23 | 81.5 |
| Kimi K2.5 | 0.71 | 77.0 | 0.73 | 70.2 | 0.78 | 78.2 | 0.72 | 77.8 | 0.66 | 86.0 | |
| Kimi K2.5 | β | 0.70 | 72.3 | 0.75 | 68.5 | 0.78 | 81.7 | 0.60 | 65.3 | 0.66 | 84.8 |
| Proprietary Models | |||||||||||
| GPT-4o | 0.30 | 55.9 | 0.35 | 20.5 | 0.47 | 83.0 | 0.24 | 55.6 | 0.12 | 80.5 | |
| GPT-5 | 0.38 | 62.1 | 0.50 | 36.2 | 0.57 | 59.6 | 0.21 | 78.1 | 0.18 | 71.0 | |
| Seed 1.8 | 0.69 | 69.6 | 0.68 | 45.5 | 0.79 | 92.7 | 0.69 | 71.8 | 0.61 | 82.5 | |
| Seed 1.8 | β | 0.67 | 71.1 | 0.69 | 48.0 | 0.78 | 89.2 | 0.57 | 73.3 | 0.60 | 80.8 |
| Seed 2.0 Pro | 0.72 | 76.1 | 0.75 | 60.8 | 0.81 | 82.0 | 0.73 | 77.6 | 0.62 | 92.2 | |
| Seed 2.0 Pro | β | 0.71 | 75.3 | 0.76 | 61.8 | 0.80 | 82.0 | 0.65 | 74.3 | 0.66 | 89.0 |
| MiMo-V2-Omni | β | 0.56 | 62.3 | 0.62 | 40.0 | 0.71 | 80.7 | 0.58 | 73.3 | 0.36 | 64.2 |
| Gemini 2.5 Pro | β | 0.53 | 56.3 | 0.67 | 33.2 | 0.72 | 39.6 | 0.49 | 59.4 | 0.23 | 95.0 |
| Gemini 3.1 Pro | β | 0.70 | 73.1 | 0.80 | 61.0 | 0.83 | 62.7 | 0.66 | 71.1 | 0.52 | 95.8 |
| Claude Opus 4.7 | β | 0.50 | 66.8 | 0.53 | 50.2 | 0.63 | 74.4 | 0.44 | 56.6 | 0.38 | 86.0 |
Cl = Clerical, Re = Regular, Ru = Running, Cu = Cursive. Bold = best.
git clone https://github.com/VirtualLUOUCAS/Chronicles-OCR.git
cd Chronicles-OCR
pip install -r requirements.txt
Download and place the benchmark data under data/:
data/
βββ Chronicles_OCR.jsonl
βββ images/
βββ η²ιͺ¨ζ/ # Oracle Bone
βββ ιζ/ # Bronze Script
βββ η―δΉ¦/ # Seal Script
βββ ιΆδΉ¦/ # Clerical Script
βββ ζ₯·δΉ¦/ # Regular Script
βββ θ‘δΉ¦/ # Running Script
βββ θδΉ¦/ # Cursive Script
# OpenAI-compatible API
python infer.py --api_type openai_compat \
--model_name Qwen2.5-VL-7B-Instruct \
--base_url http://127.0.0.1:8000/v1 \
--api_key EMPTY --max_workers 64
# Local vLLM
python infer.py --api_type local_vllm \
--model_path /path/to/model \
--tensor_parallel_size 1 --max_model_len 32768
python judge.py # all models
python judge.py --models model_a # specific model
python summarize.py
# β judge_results/results_analysis.xlsx
@misc{li2026chronicles,
title={Chronicles-OCR: A Cross-Temporal Perception Benchmark for the Evolutionary Trajectory of Chinese Characters},
author={Gengluo Li and Shangping Peng and Xingyu Wan and Chengquan Zhang and Hao Feng and Xin Xu and Pian Wu and Bang Li and Zengmao Ding and Yongge Liu and Yipei Ye and Yang Yang and Zhan Shu and Guojun Yan and Zhe Li and Can Ma and Weiping Wang and Yu Zhou and Han Hu},
year={2026},
journal={arXiv preprint arXiv:2605.11960},
url={https://arxiv.org/abs/2605.11960},
}
We sincerely acknowledge the Key Laboratory of Oracle Bone Inscription Information Processing at Anyang Normal University and the Palace Museum for their invaluable contributions to data sourcing and expert annotation.
This benchmark is released for research purposes only.
A Cross-Temporal Perception Benchmark for the Evolutionary Trajectory of Chinese Characters
δΈζη β’ Paper β’ GitHub β’ HuggingFace β’ ModelScope
Chronicles-OCR is the first comprehensive benchmark specifically designed to evaluate the cross-temporal visual perception capabilities of VLLMs across the complete evolutionary trajectory of Chinese characters β the "Seven Chinese Scripts".
Curated in collaboration with top-tier institutional domain experts (the Key Laboratory of Oracle Bone Inscription Information Processing at Anyang Normal University and the Palace Museum), the dataset comprises 2,800 strictly balanced images encompassing highly diverse physical media, ranging from tortoise shells to paper-based calligraphy.
The "Seven Chinese Scripts" (ζ±εδΈδ½) refer to the seven canonical script forms that emerged throughout the evolution of Chinese characters over more than 5,000 years:
Among these, the first five (Oracle Bone β Regular) successively served as formal writing systems in their respective eras, while Cursive and Running scripts developed primarily as auxiliary styles for informal and rapid writing.
| Item | Details |
|---|---|
| Total Images | 2,800 (400 per script Γ 7 scripts) |
| Script Coverage | All Seven Chinese Scripts |
| Annotation | Stage-Adaptive: character-level for archaic, paragraph-level for mature scripts |
| Expert Partners | Anyang Normal University (Oracle Bone), Palace Museum (ClericalβCursive) |
| Tasks | 4 evaluation tasks |
| Task | Short Name | Scope | Metric |
|---|---|---|---|
| Cross-period Character Spotting | Spotting | Oracle Bone, Bronze, Seal | F1 @ IoU > 0.75 |
| Fine-grained Archaic Character Recognition | Recognition | Oracle Bone, Bronze, Seal | Exact-match Accuracy |
| Ancient Text Parsing | Parsing | All Seven Scripts | 1 β NED (Levenshtein) |
| Script Classification | Classification | All Seven Scripts | Accuracy |
| Model | Think | Avg Spot. | Avg Fine. | Avg Pars. | Avg Class. | OB Spot. | OB Fine. | OB Pars. | OB Class. | Br Spot. | Br Fine. | Br Pars. | Br Class. | Se Spot. | Se Fine. | Se Pars. | Se Class. |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Open-Source Models | |||||||||||||||||
| InternVL3.5-8B | 0.1 | 6.0 | 0.07 | 56.7 | 0.0 | 1.1 | 0.01 | 86.2 | 0.0 | 2.2 | 0.03 | 7.0 | 0.2 | 14.5 | 0.17 | 77.0 | |
| InternVL3.5-A28B | 0.5 | 15.7 | 0.13 | 79.0 | 0.0 | 2.5 | 0.02 | 96.3 | 0.4 | 7.8 | 0.08 | 79.2 | 1.0 | 36.8 | 0.29 | 61.5 | |
| Qwen2.5-VL-7B | 0.0 | 7.4 | 0.07 | 71.8 | 0.0 | 4.0 | 0.02 | 93.8 | 0.0 | 4.5 | 0.04 | 22.5 | 0.0 | 13.8 | 0.14 | 99.2 | |
| Qwen2.5-VL-72B | 0.0 | 0.0 | 0.07 | 74.2 | 0.0 | 0.0 | 0.01 | 98.0 | 0.0 | 0.0 | 0.04 | 26.0 | 0.0 | 0.0 | 0.16 | 98.5 | |
| Qwen3-VL-2B | 2.1 | 10.7 | 0.12 | 73.0 | 0.0 | 1.4 | 0.00 | 96.6 | 0.8 | 6.8 | 0.06 | 36.5 | 5.7 | 24.0 | 0.31 | 85.8 | |
| Qwen3-VL-8B | 3.4 | 17.3 | 0.18 | 73.7 | 0.2 | 3.4 | 0.01 | 98.6 | 2.5 | 11.0 | 0.10 | 24.0 | 7.5 | 37.5 | 0.42 | 98.5 | |
| Qwen3-VL-8B | β | 1.0 | 9.1 | 0.09 | 67.3 | 0.0 | 3.7 | 0.03 | 97.7 | 0.2 | 7.0 | 0.05 | 31.8 | 2.8 | 16.8 | 0.20 | 72.5 |
| Qwen3-VL-A22B | 7.8 | 17.5 | 0.19 | 91.8 | 0.3 | 5.4 | 0.01 | 99.2 | 6.5 | 12.2 | 0.12 | 80.2 | 16.6 | 35.0 | 0.43 | 96.0 | |
| Qwen3-VL-A22B | β | 2.1 | 13.6 | 0.17 | 87.3 | 0.1 | 4.2 | 0.03 | 98.0 | 0.9 | 10.2 | 0.11 | 66.8 | 5.3 | 26.2 | 0.37 | 97.2 |
| Qwen3.5-A3B | 5.6 | 16.2 | 0.20 | 76.5 | 0.2 | 5.1 | 0.02 | 99.7 | 5.3 | 11.5 | 0.12 | 30.0 | 11.2 | 32.0 | 0.45 | 99.8 | |
| Qwen3.5-A17B | 9.7 | 22.6 | 0.22 | 88.3 | 0.5 | 9.1 | 0.02 | 99.7 | 9.2 | 17.5 | 0.13 | 67.2 | 19.4 | 41.3 | 0.50 | 98.0 | |
| Gemma 4 31B it | 2.3 | 7.0 | 0.04 | 70.0 | 0.0 | 3.1 | 0.01 | 72.6 | 1.0 | 6.5 | 0.03 | 74.8 | 6.0 | 11.2 | 0.10 | 62.7 | |
| MiniCPM-V 4.5 | β | 0.0 | 4.8 | 0.02 | 73.8 | 0.0 | 2.5 | 0.01 | 95.2 | 0.0 | 5.5 | 0.03 | 18.0 | 0.1 | 9.0 | 0.04 | 82.5 |
| Molmo 7B-D 0924 | 0.0 | 0.1 | 0.00 | 24.2 | 0.0 | 0.0 | 0.01 | 40.8 | 0.0 | 0.2 | 0.00 | 0.0 | 0.0 | 0.0 | 0.00 | 20.5 | |
| Molmo 72B 0924 | 0.0 | 0.3 | 0.00 | 34.7 | 0.0 | 0.5 | 0.00 | 28.0 | 0.0 | 0.5 | 0.00 | 0.8 | 0.0 | 0.0 | 0.00 | 82.0 | |
| Ovis2.6-30B-A3B | β | 1.9 | 9.0 | 0.09 | 68.3 | 0.1 | 2.0 | 0.01 | 89.8 | 0.7 | 7.5 | 0.06 | 13.5 | 6.8 | 24.5 | 0.25 | 79.0 |
| GLM-4.5V 108B | β | 1.4 | 6.1 | 0.05 | 76.8 | 0.1 | 4.2 | 0.03 | 100 | 2.0 | 6.5 | 0.05 | 15.5 | 3.3 | 9.2 | 0.10 | 91.5 |
| Kimi K2.5 | 5.0 | 27.1 | 0.22 | 96.4 | 0.1 | 11.5 | 0.05 | 100 | 7.5 | 25.8 | 0.19 | 90.0 | 12.5 | 58.5 | 0.60 | 95.5 | |
| Kimi K2.5 | β | 1.8 | 20.3 | 0.22 | 94.7 | 0.0 | 10.2 | 0.05 | 99.8 | 1.2 | 17.5 | 0.20 | 85.8 | 6.0 | 44.8 | 0.57 | 93.5 |
| Proprietary Models | |||||||||||||||||
| GPT-4o | 0.1 | 1.5 | 0.02 | 82.0 | 0.0 | 0.5 | 0.01 | 96.5 | 0.0 | 1.0 | 0.02 | 46.8 | 0.3 | 4.5 | 0.06 | 89.0 | |
| GPT-5 | 0.4 | 3.7 | 0.04 | 88.1 | 0.0 | 4.0 | 0.00 | 98.2 | 0.0 | 4.0 | 0.04 | 60.5 | 1.6 | 4.5 | 0.12 | 97.5 | |
| Seed 1.8 | 9.2 | 20.6 | 0.16 | 94.7 | 0.4 | 9.2 | 0.03 | 99.5 | 9.4 | 15.8 | 0.17 | 80.5 | 26.7 | 45.0 | 0.42 | 99.0 | |
| Seed 1.8 | β | 7.4 | 17.1 | 0.17 | 96.7 | 0.4 | 8.8 | 0.04 | 99.5 | 5.8 | 14.8 | 0.18 | 90.0 | 23.3 | 36.2 | 0.43 | 97.5 |
| Seed 2.0 Pro | 16.5 | 24.5 | 0.18 | 95.9 | 3.0 | 11.0 | 0.03 | 99.5 | 19.9 | 30.8 | 0.22 | 92.2 | 40.7 | 41.5 | 0.43 | 93.8 | |
| Seed 2.0 Pro | β | 15.3 | 23.3 | 0.21 | 96.6 | 2.4 | 11.2 | 0.04 | 99.8 | 17.8 | 26.0 | 0.26 | 92.2 | 39.1 | 37.5 | 0.49 | 94.5 |
| MiMo-V2-Omni | β | 0.4 | 8.6 | 0.08 | 87.7 | 0.0 | 6.5 | 0.04 | 99.5 | 0.2 | 8.0 | 0.07 | 58.5 | 1.5 | 9.8 | 0.15 | 93.0 |
| Gemini 2.5 Pro | β | 0.8 | 7.5 | 0.07 | 87.5 | 0.0 | 5.8 | 0.04 | 99.5 | 0.2 | 7.0 | 0.06 | 80.5 | 2.8 | 10.8 | 0.14 | 70.2 |
| Gemini 3.1 Pro | β | 2.6 | 19.5 | 0.15 | 93.8 | 0.0 | 14.0 | 0.05 | 99.5 | 2.5 | 22.5 | 0.18 | 84.5 | 7.8 | 32.2 | 0.32 | 93.2 |
| Claude Opus 4.7 | β | 0.4 | 10.0 | 0.08 | 90.4 | 0.0 | 4.8 | 0.03 | 93.8 | 0.1 | 9.5 | 0.05 | 80.5 | 1.4 | 21.5 | 0.21 | 93.8 |
OB = Oracle Bone, Br = Bronze, Se = Seal. Bold = best, scores are H-mean (Spot.), Accuracy (Fine./Class.), NED (Pars.).
| Model | Think | Avg Pars. | Avg Class. | Cl Pars. | Cl Class. | Re Pars. | Re Class. | Ru Pars. | Ru Class. | Cu Pars. | Cu Class. |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Open-Source Models | |||||||||||
| InternVL3.5-8B | 0.40 | 35.6 | 0.41 | 1.8 | 0.51 | 69.4 | 0.38 | 52.9 | 0.30 | 35.0 | |
| InternVL3.5-A28B | 0.56 | 58.1 | 0.54 | 28.5 | 0.69 | 85.5 | 0.56 | 63.3 | 0.46 | 75.2 | |
| Qwen2.5-VL-7B | 0.44 | 34.8 | 0.54 | 8.0 | 0.62 | 17.0 | 0.42 | 36.4 | 0.21 | 90.5 | |
| Qwen2.5-VL-72B | 0.49 | 57.2 | 0.59 | 18.0 | 0.66 | 91.5 | 0.46 | 56.6 | 0.26 | 86.0 | |
| Qwen3-VL-2B | 0.57 | 35.2 | 0.61 | 5.5 | 0.71 | 11.8 | 0.50 | 37.9 | 0.42 | 93.0 | |
| Qwen3-VL-8B | 0.66 | 60.9 | 0.69 | 32.5 | 0.77 | 97.2 | 0.64 | 59.1 | 0.56 | 81.0 | |
| Qwen3-VL-8B | β | 0.49 | 45.9 | 0.52 | 11.2 | 0.64 | 79.7 | 0.51 | 53.4 | 0.32 | 56.2 |
| Qwen3-VL-A22B | 0.66 | 64.9 | 0.69 | 36.5 | 0.73 | 95.5 | 0.66 | 68.3 | 0.59 | 82.0 | |
| Qwen3-VL-A22B | β | 0.65 | 60.4 | 0.67 | 31.0 | 0.75 | 93.5 | 0.65 | 62.3 | 0.54 | 78.0 |
| Qwen3.5-A3B | 0.71 | 68.1 | 0.79 | 36.8 | 0.81 | 84.2 | 0.68 | 75.6 | 0.57 | 84.2 | |
| Qwen3.5-A17B | 0.73 | 72.2 | 0.81 | 52.0 | 0.81 | 81.3 | 0.67 | 75.3 | 0.66 | 89.4 | |
| Gemma 4 31B it | 0.34 | 57.1 | 0.37 | 9.6 | 0.56 | 81.9 | 0.33 | 65.0 | 0.09 | 84.5 | |
| MiniCPM-V 4.5 | β | 0.40 | 44.9 | 0.45 | 2.8 | 0.61 | 87.5 | 0.38 | 56.9 | 0.15 | 48.8 |
| Molmo 7B-D 0924 | 0.01 | 16.9 | 0.01 | 70.8 | 0.01 | 3.0 | 0.01 | 0.7 | 0.01 | 0.5 | |
| Molmo 72B 0924 | 0.00 | 9.1 | 0.00 | 6.8 | 0.01 | 16.5 | 0.01 | 3.2 | 0.00 | 12.8 | |
| Ovis2.6-30B-A3B | β | 0.53 | 39.7 | 0.54 | 8.5 | 0.63 | 77.9 | 0.57 | 71.6 | 0.42 | 12.2 |
| GLM-4.5V 108B | β | 0.44 | 56.6 | 0.45 | 11.5 | 0.61 | 84.5 | 0.44 | 63.3 | 0.23 | 81.5 |
| Kimi K2.5 | 0.71 | 77.0 | 0.73 | 70.2 | 0.78 | 78.2 | 0.72 | 77.8 | 0.66 | 86.0 | |
| Kimi K2.5 | β | 0.70 | 72.3 | 0.75 | 68.5 | 0.78 | 81.7 | 0.60 | 65.3 | 0.66 | 84.8 |
| Proprietary Models | |||||||||||
| GPT-4o | 0.30 | 55.9 | 0.35 | 20.5 | 0.47 | 83.0 | 0.24 | 55.6 | 0.12 | 80.5 | |
| GPT-5 | 0.38 | 62.1 | 0.50 | 36.2 | 0.57 | 59.6 | 0.21 | 78.1 | 0.18 | 71.0 | |
| Seed 1.8 | 0.69 | 69.6 | 0.68 | 45.5 | 0.79 | 92.7 | 0.69 | 71.8 | 0.61 | 82.5 | |
| Seed 1.8 | β | 0.67 | 71.1 | 0.69 | 48.0 | 0.78 | 89.2 | 0.57 | 73.3 | 0.60 | 80.8 |
| Seed 2.0 Pro | 0.72 | 76.1 | 0.75 | 60.8 | 0.81 | 82.0 | 0.73 | 77.6 | 0.62 | 92.2 | |
| Seed 2.0 Pro | β | 0.71 | 75.3 | 0.76 | 61.8 | 0.80 | 82.0 | 0.65 | 74.3 | 0.66 | 89.0 |
| MiMo-V2-Omni | β | 0.56 | 62.3 | 0.62 | 40.0 | 0.71 | 80.7 | 0.58 | 73.3 | 0.36 | 64.2 |
| Gemini 2.5 Pro | β | 0.53 | 56.3 | 0.67 | 33.2 | 0.72 | 39.6 | 0.49 | 59.4 | 0.23 | 95.0 |
| Gemini 3.1 Pro | β | 0.70 | 73.1 | 0.80 | 61.0 | 0.83 | 62.7 | 0.66 | 71.1 | 0.52 | 95.8 |
| Claude Opus 4.7 | β | 0.50 | 66.8 | 0.53 | 50.2 | 0.63 | 74.4 | 0.44 | 56.6 | 0.38 | 86.0 |
Cl = Clerical, Re = Regular, Ru = Running, Cu = Cursive. Bold = best.
git clone https://github.com/VirtualLUOUCAS/Chronicles-OCR.git
cd Chronicles-OCR
pip install -r requirements.txt
Download and place the benchmark data under data/:
data/
βββ Chronicles_OCR.jsonl
βββ images/
βββ η²ιͺ¨ζ/ # Oracle Bone
βββ ιζ/ # Bronze Script
βββ η―δΉ¦/ # Seal Script
βββ ιΆδΉ¦/ # Clerical Script
βββ ζ₯·δΉ¦/ # Regular Script
βββ θ‘δΉ¦/ # Running Script
βββ θδΉ¦/ # Cursive Script
# OpenAI-compatible API
python infer.py --api_type openai_compat \
--model_name Qwen2.5-VL-7B-Instruct \
--base_url http://127.0.0.1:8000/v1 \
--api_key EMPTY --max_workers 64
# Local vLLM
python infer.py --api_type local_vllm \
--model_path /path/to/model \
--tensor_parallel_size 1 --max_model_len 32768
python judge.py # all models
python judge.py --models model_a # specific model
python summarize.py
# β judge_results/results_analysis.xlsx
@misc{li2026chronicles,
title={Chronicles-OCR: A Cross-Temporal Perception Benchmark for the Evolutionary Trajectory of Chinese Characters},
author={Gengluo Li and Shangping Peng and Xingyu Wan and Chengquan Zhang and Hao Feng and Xin Xu and Pian Wu and Bang Li and Zengmao Ding and Yongge Liu and Yipei Ye and Yang Yang and Zhan Shu and Guojun Yan and Zhe Li and Can Ma and Weiping Wang and Yu Zhou and Han Hu},
year={2026},
journal={arXiv preprint arXiv:2605.11960},
url={https://arxiv.org/abs/2605.11960},
}
We sincerely acknowledge the Key Laboratory of Oracle Bone Inscription Information Processing at Anyang Normal University and the Palace Museum for their invaluable contributions to data sourcing and expert annotation.
This benchmark is released for research purposes only.