Update README.md

README.md

@@ -46,7 +46,6 @@ Dolphin-v2 follows a document-type-aware two-stage paradigm:
 ### Stage 1: Joint Classification and Layout Analysis
 - **Document Type Classification**: Distinguishes between digital-born and photographed documents
 - **Layout Analysis**: Generates element sequences in reading order with 21 supported categories
-- **Precise Localization**: Absolute coordinate system for pixel-level accuracy
 
 ### Stage 2: Hybrid Content Parsing
 - **Photographed Documents**: Holistic page-level parsing to handle distortions
@@ -63,7 +62,6 @@ Built on **Qwen2.5-VL-3B** backbone with:
 ## 📈 Performance
 
 Dolphin-v2 achieves superior performance on comprehensive benchmarks:
-
 **OmniDocBench (v1.5):**
 - Overall Score: **89.45** (+14.78 over original Dolphin)
 - Text Recognition: **0.054** Edit Distance
@@ -73,7 +71,6 @@ Dolphin-v2 achieves superior performance on comprehensive benchmarks:
 
 
 ## 🎯 Supported Element Types
-
 Dolphin-v2 supports 21 document element categories:
 
 | Element Type | Description |
@@ -94,39 +91,6 @@ Dolphin-v2 supports 21 document element categories:
 | `watermark` | Watermarks |
 | `anno` | Annotations |
 
-## 💻 Usage
-Please refer to our [GitHub repository](https://github.com/bytedance/Dolphin) for detailed usage:
-- Page-wise parsing for complete document images
-- Element-wise parsing for specific regions
-- Examples for digital and photographed documents
-
-## 🔧 Training Details
-
-- **Backbone**: Qwen2.5-VL-3B
-- **Training Data**: 
-  - 200K photographed documents with realistic distortions
-  - 200K code images (C++, Python, Go, JavaScript)
-  - 200K catalog images with hierarchical structures
-- **Optimizer**: AdamW (lr=8e-5, weight decay=0)
-- **Training**: 10 epochs on 40 A100 GPUs
-- **Max Sequence Length**: 131,072 tokens
-
-## 📊 Benchmarks
-
-We evaluate on two complementary benchmarks:
-- **OmniDocBench**: Diverse document types (academic papers, textbooks, slides, reports)
-- **RealDoc-160**: Real-world photographed documents with authentic distortions
-
-## 🚀 Key Features
-
-✅ Handles both digital and photographed documents seamlessly  
-✅ 21 element categories with fine-grained detection  
-✅ Precise LaTeX formula recognition  
-✅ Code block parsing with indentation preservation  
-✅ Robust to distortions, lighting variations, and perspective changes  
-✅ Efficient parallel processing for digital documents  
-✅ Lightweight 3B parameter model  
-
 
 ## 📚 Citation
 ```bibtex