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README.md

@@ -1,23 +1,103 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: filename
-    dtype: string
-  - name: paddleocr_chart
-    dtype: string
-  - name: inference_info
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 17170041
-    num_examples: 13
-  download_size: 16318523
-  dataset_size: 17170041
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+tags:
+- ocr
+- document-processing
+- paddleocr-vl
+- chart
+- uv-script
+- generated
 ---
+
+# Document Processing using PaddleOCR-VL (CHART mode)
+
+This dataset contains CHART results from images in [minhpvo/ocr-input](https://huggingface.co/datasets/minhpvo/ocr-input) using PaddleOCR-VL, an ultra-compact 0.9B OCR model.
+
+## Processing Details
+
+- **Source Dataset**: [minhpvo/ocr-input](https://huggingface.co/datasets/minhpvo/ocr-input)
+- **Model**: [PaddlePaddle/PaddleOCR-VL](https://huggingface.co/PaddlePaddle/PaddleOCR-VL)
+- **Task Mode**: `chart` - Chart and diagram analysis
+- **Number of Samples**: 13
+- **Processing Time**: 1.9 min
+- **Processing Date**: 2026-02-09 04:08 UTC
+
+### Configuration
+
+- **Image Column**: `image`
+- **Output Column**: `paddleocr_chart`
+- **Dataset Split**: `train`
+- **Batch Size**: 16
+- **Smart Resize**: Enabled
+- **Max Model Length**: 8,192 tokens
+- **Max Output Tokens**: 4,096
+- **Temperature**: 0.0
+- **GPU Memory Utilization**: 80.0%
+
+## Model Information
+
+PaddleOCR-VL is a state-of-the-art, resource-efficient model tailored for document parsing:
+- 🎯 **Ultra-compact** - Only 0.9B parameters (smallest OCR model)
+- 📝 **OCR mode** - General text extraction
+- 📊 **Table mode** - HTML table recognition
+- 📐 **Formula mode** - LaTeX mathematical notation
+- 📈 **Chart mode** - Structured chart analysis
+- 🌍 **Multilingual** - Support for multiple languages
+- ⚡ **Fast** - Quick initialization and inference
+- 🔧 **ERNIE-4.5 based** - Different architecture from Qwen models
+
+### Task Modes
+
+- **OCR**: Extract text content to markdown format
+- **Table Recognition**: Extract tables to HTML format
+- **Formula Recognition**: Extract mathematical formulas to LaTeX
+- **Chart Recognition**: Analyze and describe charts/diagrams
+
+## Dataset Structure
+
+The dataset contains all original columns plus:
+- `paddleocr_chart`: The extracted content based on task mode
+- `inference_info`: JSON list tracking all OCR models applied to this dataset
+
+## Usage
+
+```python
+from datasets import load_dataset
+import json
+
+# Load the dataset
+dataset = load_dataset("{output_dataset_id}", split="train")
+
+# Access the extracted content
+for example in dataset:
+    print(example["paddleocr_chart"])
+    break
+
+# View all OCR models applied to this dataset
+inference_info = json.loads(dataset[0]["inference_info"])
+for info in inference_info:
+    print(f"Task: {info['task_mode']} - Model: {info['model_id']}")
+```
+
+## Reproduction
+
+This dataset was generated using the [uv-scripts/ocr](https://huggingface.co/datasets/uv-scripts/ocr) PaddleOCR-VL script:
+
+```bash
+uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl.py \
+    minhpvo/ocr-input \
+    <output-dataset> \
+    --task-mode chart \
+    --image-column image \
+    --batch-size 16 \
+    --max-model-len 8192 \
+    --max-tokens 4096 \
+    --gpu-memory-utilization 0.8
+```
+
+## Performance
+
+- **Model Size**: 0.9B parameters (smallest among OCR models)
+- **Processing Speed**: ~0.11 images/second
+- **Architecture**: NaViT visual encoder + ERNIE-4.5-0.3B language model
+
+Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)