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README.md

@@ -1,21 +1,98 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: markdown
-    dtype: string
-  - name: inference_info
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 146150
-    num_examples: 1
-  download_size: 149864
-  dataset_size: 146150
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+tags:
+- ocr
+- document-processing
+- deepseek
+- deepseek-ocr
+- markdown
+- uv-script
+- generated
 ---
+
+# Document OCR using DeepSeek-OCR
+
+This dataset contains markdown-formatted OCR results from images in [Alysonhower/test](https://huggingface.co/datasets/Alysonhower/test) using DeepSeek-OCR.
+
+## Processing Details
+
+- **Source Dataset**: [Alysonhower/test](https://huggingface.co/datasets/Alysonhower/test)
+- **Model**: [deepseek-ai/DeepSeek-OCR](https://huggingface.co/deepseek-ai/DeepSeek-OCR)
+- **Number of Samples**: 1
+- **Processing Time**: 1.5 minutes
+- **Processing Date**: 2025-10-23 13:06 UTC
+
+### Configuration
+
+- **Image Column**: `image`
+- **Output Column**: `markdown`
+- **Dataset Split**: `train`
+- **Resolution Mode**: gundam
+- **Base Size**: 1024
+- **Image Size**: 640
+- **Crop Mode**: True
+
+## Model Information
+
+DeepSeek-OCR is a state-of-the-art document OCR model that excels at:
+- 📐 **LaTeX equations** - Mathematical formulas preserved in LaTeX format
+- 📊 **Tables** - Extracted and formatted as HTML/markdown
+- 📝 **Document structure** - Headers, lists, and formatting maintained
+- 🖼️ **Image grounding** - Spatial layout and bounding box information
+- 🔍 **Complex layouts** - Multi-column and hierarchical structures
+- 🌍 **Multilingual** - Supports multiple languages
+
+### Resolution Modes
+
+- **Tiny** (512×512): Fast processing, 64 vision tokens
+- **Small** (640×640): Balanced speed/quality, 100 vision tokens
+- **Base** (1024×1024): High quality, 256 vision tokens
+- **Large** (1280×1280): Maximum quality, 400 vision tokens
+- **Gundam** (dynamic): Adaptive multi-tile processing for large documents
+
+## Dataset Structure
+
+The dataset contains all original columns plus:
+- `markdown`: The extracted text in markdown format with preserved structure
+- `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 markdown text
+for example in dataset:
+    print(example["markdown"])
+    break
+
+# View all OCR models applied to this dataset
+inference_info = json.loads(dataset[0]["inference_info"])
+for info in inference_info:
+    print(f"Column: {{info['column_name']}} - Model: {{info['model_id']}}")
+```
+
+## Reproduction
+
+This dataset was generated using the [uv-scripts/ocr](https://huggingface.co/datasets/uv-scripts/ocr) DeepSeek OCR script:
+
+```bash
+uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr.py \
+    Alysonhower/test \
+    <output-dataset> \
+    --resolution-mode gundam \
+    --image-column image
+```
+
+## Performance
+
+- **Processing Speed**: ~0.0 images/second
+- **Processing Method**: Sequential (Transformers API, no batching)
+
+Note: This uses the official Transformers implementation. For faster batch processing,
+consider using the vLLM version once DeepSeek-OCR is officially supported by vLLM.
+
+Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)