metadata
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
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.