Update README.md

README.md

@@ -7,84 +7,59 @@ language:
 - zh
 - en
 configs:
-- config_name: easy
+- config_name: Full-Page OCR  # 例如：default
   data_files:
   - split: test
-    path: data/easy/*.parquet
-- config_name: medium
-  data_files:
-  - split: test
-    path: data/medium/*.parquet
-- config_name: hard
-  data_files:
-  - split: test
-    path: data/hard/*.parquet
+    path: 
+      - data/easy/*.parquet  # 指定easy子文件夹下所有.parquet文件
+      - data/medium/*.parquet  # 指定medium子文件夹下所有.parquet文件
+      - data/hard/*.parquet  # 指定hard子文件夹下所有.parquet文件
 tags:
 - art
-- calligraphy
-- chinese
-- visual-language-model
-- document-understanding
 size_categories:
 - 1K<n<10K
+
 ---
 
-# 🧠 CalliReader: Contextualizing Chinese Calligraphy via an Embedding-aligned Vision Language Model
 
+# 🧠 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://github.com/LoYuXr/CalliReader">📂 Code</a>
   <a href="https://arxiv.org/pdf/2503.06472">📄 Paper</a>
 </div>
 
-## Dataset Description
+**CalliBench** is aimed to comprehensively evaluate VLMs' performance on the recognition and understanding of Chinese calligraphy. 
 
-**CalliBench** is a comprehensive benchmark designed to evaluate Vision-Language Models (VLMs) on the recognition and understanding of Chinese calligraphy.
 
-### Dataset Subsets
 
-CalliBench contains three distinct subsets organized by difficulty level:
+## 📦 Dataset Summary
 
-#### 🟢 Easy Subset
-- **Samples**: ~1,000 image-annotation pairs
-- **Characteristics**: Standard script styles, clear layouts, well-known works
-- **Use Case**: Basic calligraphy recognition and transcription
+* **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**.
 
-#### 🟡 Medium Subset  
-- **Samples**: ~1,000 image-annotation pairs
-- **Characteristics**: Mixed script styles, moderate complexity, historical variants
-- **Use Case**: Intermediate-level analysis and contextual understanding
+## 🧪 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.
 
-#### 🔴 Hard Subset
-- **Samples**: ~1,192 image-annotation pairs
-- **Characteristics**: Cursive scripts, complex layouts, rare characters, damaged works
-- **Use Case**: Advanced contextual reasoning and expert-level interpretation
-
-### Dataset Summary
-
-- **Total Samples**: 3,192 image–annotation pairs
-- **Tasks**: 
-  - **Full-page recognition**: Complete transcription of calligraphy works
-  - **Contextual VQA**: Author identification, layout analysis, style classification, bilingual interpretation, and intent analysis
-
-### Usage Examples
-
-#### Loading Specific Subsets
-
-```python
+For example, to load a sample and convert its image into a .jpg file: 
+```
 import pandas as pd
 import io
 from PIL import Image
 
-# Load easy subset
-df_easy = pd.read_parquet('./data/easy/easy.parquet')
+df = pd.read_parquet('./data/hard/hard.parquet')
+
+image_data = df.iloc[0]['image'] 
+image = Image.open(io.BytesIO(image_data['bytes']))
+
+image.save('output_image.jpg') 
 
-# Load medium subset  
-df_medium = pd.read_parquet('./data/medium/medium.parquet')
+```
 
-# Load hard subset
-df_hard = pd.read_parquet('./data/hard/hard.parquet')
+## 🤗 License
 
-# Access sample data
-sample = df_hard.iloc[0]
-image_data = sample['image']
-image = Image.open(io.BytesIO(image_data['bytes']))
+Apache 2.0 – open for research and commercial use.