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