README.md

metadata

license: apache-2.0
task_categories:
  - image-to-text
  - visual-question-answering
language:
  - zh
  - en
configs:
  - config_name: Full_page_ocr
    data_files:
      - split: test
        path:
          - full_page_ocr/easy/easy.parquet
          - full_page_ocr/medium/medium.parquet
          - full_page_ocr/hard/hard.parquet
  - config_name: Intent
    data_files:
      - split: test
        path:
          - reasoning/intent/intent.parquet
  - config_name: Bilingual
    data_files:
      - split: test
        path:
          - reasoning/bilingual/medium/bilingual_medium.parquet
          - reasoning/bilingual/hard/bilingual_hard.parquet
  - config_name: Author
    data_files:
      - split: test
        path:
          - choice/author/author.parquet
  - config_name: Style
    data_files:
      - split: test
        path:
          - choice/style/style.parquet
  - config_name: Layout
    data_files:
      - split: test
        path:
          - choice/layout/layout.parquet
  - config_name: Region
    data_files:
      - split: test
        path:
          - region-wise/region.parquet
dataset_info:
  - config_name: Full_page_ocr
    features:
      - name: id
        dtype: string
      - name: image
        dtype: image
      - name: answer
        dtype: string
      - name: annotation
        dtype: string
    splits:
      - name: test
  - config_name: Intent
    features:
      - name: id
        dtype: string
      - name: image
        dtype: image
      - name: answer
        dtype: string
      - name: annotation
        dtype: string
    splits:
      - name: test
  - config_name: Bilingual
    features:
      - name: id
        dtype: string
      - name: image
        dtype: image
      - name: answer
        dtype: string
      - name: annotation
        dtype: string
    splits:
      - name: test
  - config_name: Author
    features:
      - name: id
        dtype: string
      - name: image
        dtype: image
      - name: question
        dtype: string
      - name: answer
        dtype: string
      - name: annotation
        dtype: string
    splits:
      - name: test
  - config_name: Style
    features:
      - name: id
        dtype: string
      - name: image
        dtype: image
      - name: question
        dtype: string
      - name: answer
        dtype: string
      - name: annotation
        dtype: string
    splits:
      - name: test
  - config_name: Layout
    features:
      - name: id
        dtype: string
      - name: image
        dtype: image
      - name: question
        dtype: string
      - name: answer
        dtype: string
      - name: annotation
        dtype: string
    splits:
      - name: test
  - config_name: Region
    features:
      - name: id
        dtype: string
      - name: image
        dtype: image
      - name: region
        dtype: string
      - name: answer
        dtype: string
      - name: annotation
        dtype: string
    splits:
      - name: test
tags:
  - art
size_categories:
  - 1K<n<10K

🧠 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.