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---
license: apache-2.0
task_categories:
- image-to-text
- visual-question-answering
language:
- zh
- en
tags:
- art
size_categories:
- 1K<n<10K
---
# 🧠 CalliReader: Contextualizing Chinese Calligraphy via an Embedding-aligned Vision Language Model
<div align="center">
<a href="https://github.com/LoYuXr/CalliReader">📂 Code</a>
<a href="https://arxiv.org/pdf/2503.06472">📄 Paper</a>
</div>
**CalliBench** is aimed to comprehensively evaluate VLMs' performance on the recognition and understanding of Chinese calligraphy.
## 📦 Dataset Summary
* **Samples**: 3,192 image–annotation pairs
* **Tasks**: **Full-page recognition** and **Contextual VQA** (choice of author/layout/style, bilingual interpretation, and intent analysis).
* **Annotations**:
* Metadata of author, layout, and style.
* Fine-grained annotations of **character-wise bounding boxes and labels**.
* Certain samples include **contextual VQA**.
## 🧪 How To Use
All **.parqeut** files of different tiers can be found in the sub-folders of **data**. **Pandas** can be used to parse and further process those files.
For example, to load a sample and convert its image into a .jpg file:
```
import pandas as pd
import io
from PIL import Image
df = pd.read_parquet('./data/train.parquet')
image_data = df.iloc[0]['image']
image = Image.open(io.BytesIO(image_data['bytes']))
image.save('output_image.jpg')
```
## 🤗 License
Apache 2.0 – open for research and commercial use. |