gtang666/CalliBench

🧠 CalliReader: Contextualizing Chinese Calligraphy via an Embedding-aligned Vision Language Model

📂 Code 📄 Paper

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('./full_page_ocr/hard/hard.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.