README.md

---
tags:
- ocr
- document-processing
- olmocr
- markdown
- uv-script
- generated
configs:
- config_name: dots-mocr
  data_files:
  - split: train
    path: dots-mocr/train-*
dataset_info:
  config_name: dots-mocr
  features:
  - name: image
    dtype: image
  - name: volume
    dtype: int64
  - name: volume_label
    dtype: string
  - name: leaf_number
    dtype: int64
  - name: page_number
    dtype: string
  - name: page_number_confidence
    dtype: int64
  - name: page_type
    dtype: string
  - name: width
    dtype: int64
  - name: height
    dtype: int64
  - name: ocr_text
    dtype: string
  - name: markdown
    dtype: string
  - name: inference_info
    dtype: string
  splits:
  - name: train
    num_bytes: 841831
    num_examples: 5
  download_size: 847127
  dataset_size: 841831
---

# Document OCR using olmOCR-2-7B-1025-FP8

This dataset contains markdown-formatted OCR results from images in [davanstrien/encyclopaedia-britannica-1771](https://huggingface.co/datasets/davanstrien/encyclopaedia-britannica-1771) using olmOCR-2-7B.

## Processing Details

- **Source Dataset**: [davanstrien/encyclopaedia-britannica-1771](https://huggingface.co/datasets/davanstrien/encyclopaedia-britannica-1771)
- **Model**: [allenai/olmOCR-2-7B-1025-FP8](https://huggingface.co/allenai/olmOCR-2-7B-1025-FP8)
- **Number of Samples**: 5
- **Processing Time**: 0h 3m 18s
- **Processing Date**: 2026-07-08 06:41 UTC

### Configuration

- **Image Column**: `image`
- **Output Column**: `markdown`
- **Dataset Split**: `train`
- **Batch Size**: 16
- **Max Model Length**: 16,384 tokens
- **Max Output Tokens**: 8,192
- **GPU Memory Utilization**: 80.0%

## Model Information

olmOCR-2-7B is a high-quality document OCR model based on Qwen2.5-VL-7B-Instruct, fine-tuned on olmOCR-mix-1025 dataset and optimized with GRPO reinforcement learning.

Key features:
- 📐 **LaTeX equations** - Mathematical formulas in LaTeX format
- 📊 **HTML tables** - Structured table extraction
- 📝 **Document structure** - Headers, lists, formatting preserved
- 🖼️ **Figure descriptions** - Charts and figures labeled with descriptions
- 🔄 **Rotation detection** - Metadata about document orientation
- 📑 **Natural reading order** - Handles multi-column and complex layouts
- 🎯 **High accuracy** - Scores 82.4 ± 1.1 on olmOCR-Bench

## Output Format

Each row contains:
- Original image from source dataset
- `markdown`: Extracted document content in markdown format
- `olmocr_metadata`: JSON with document metadata (language, rotation, table/diagram flags)

## Columns

- `image`: Original document image
- `markdown`: Extracted text and structure in markdown
- `olmocr_metadata`: Document metadata (primary_language, is_rotation_valid, rotation_correction, is_table, is_diagram)
- `inference_info`: Processing metadata (model, script version, timestamp)

## Reproduction

```bash
# Using HF Jobs (recommended)
hf jobs uv run --flavor l4x1 \
  -s HF_TOKEN \
  https://huggingface.co/datasets/uv-scripts/ocr/raw/main/olmocr2-vllm.py \
  davanstrien/encyclopaedia-britannica-1771 \
  your-username/output-dataset

# Local with GPU
uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/olmocr2-vllm.py \
  davanstrien/encyclopaedia-britannica-1771 \
  your-username/output-dataset
```

## Citation

```bibtex
@misc{olmocr,
      title={{olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models}},
      author={Jake Poznanski and Jon Borchardt and Jason Dunkelberger and Regan Huff and Daniel Lin and Aman Rangapur and Christopher Wilhelm and Kyle Lo and Luca Soldaini},
      year={2025},
      eprint={2502.18443},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.18443},
}
```

---
*Generated with [uv-scripts/ocr](https://huggingface.co/datasets/uv-scripts/ocr)*