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# dotsocr_markdown_dataset
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## Dataset Description
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This dataset contains training data for DotsOCR to convert document images directly to markdown format.
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## Training Objective
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The model learns to:
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- Convert document images to clean markdown format
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- Preserve document structure and hierarchy
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- Extract all text content accurately
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- Use appropriate markdown formatting for different content types
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## Dataset Structure
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- **Training samples**: 798
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- **Validation samples**: 200
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- **Total samples**: 998
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## Files
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- `data/dotsocr_markdown_dataset_train.json`: Training data in conversation format
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- `data/dotsocr_markdown_dataset_val.json`: Validation data in conversation format
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- `images/`: Directory containing all referenced images
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## Data Format
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Each sample follows this conversation format:
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```json
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{
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"conversations": [
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{
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"from": "user",
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"value": "<|imgpad|>images/example.jpg<|/imgpad|>\nPlease convert this document image to markdown format..."
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},
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{
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"from": "assistant",
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"value": "# Document Title\n\n## Section Header\n\nContent text here..."
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}
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]
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}
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```
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## Training Prompt
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The training uses this prompt to teach the model markdown conversion:
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```
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Convert this document image to markdown format. Extract and structure the text content as follows:
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- Headers and titles as plain text on separate lines
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- Body text as plain text paragraphs
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- Lists using * for bullet points
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- Tables using HTML table format: <table><thead><tr><th>...</th></tr></thead><tbody><tr><td>...</td></tr></tbody></table>
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- Preserve the original reading order and document structure
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- Extract all visible text exactly as it appears
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Output the content in markdown format with HTML tables for tabular data.
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```
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## Usage
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This dataset is designed to train DotsOCR models for direct image-to-markdown conversion tasks. The model learns to:
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1. Analyze document images
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2. Extract text content in reading order
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3. Apply appropriate markdown formatting
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4. Output clean, structured markdown
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## Citation
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Based on the DotsOCR model by rednote-hilab: https://github.com/rednote-hilab/dots.ocr
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Generated on: 2025-09-26 17:19:53
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