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--- |
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viewer: false |
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tags: [uv-script, ocr, vision-language-model, document-processing] |
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--- |
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# OCR UV Scripts |
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> Part of [uv-scripts](https://huggingface.co/uv-scripts) - ready-to-run ML tools powered by UV |
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Ready-to-run OCR scripts that work with `uv run` - no setup required! |
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## 🚀 Quick Start with HuggingFace Jobs |
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Run OCR on any dataset without needing your own GPU: |
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```bash |
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# Quick test with 10 samples |
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hf jobs uv run --flavor l4x1 \ |
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--secrets HF_TOKEN \ |
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https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \ |
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your-input-dataset your-output-dataset \ |
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--max-samples 10 |
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``` |
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That's it! The script will: |
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- ✅ Process first 10 images from your dataset |
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- ✅ Add OCR results as a new `markdown` column |
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- ✅ Push the results to a new dataset |
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- 📊 View results at: `https://huggingface.co/datasets/[your-output-dataset]` |
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## 📋 Available Scripts |
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### LightOnOCR (`lighton-ocr.py`) ⚡ Good one to test first since it's small and fast! |
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Fast and compact OCR using [lightonai/LightOnOCR-1B-1025](https://huggingface.co/lightonai/LightOnOCR-1B-1025): |
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- ⚡ **Fastest**: 5.71 pages/sec on H100, ~6.25 images/sec on A100 with batch_size=4096 |
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- 🎯 **Compact**: Only 1B parameters - quick to download and initialize |
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- 🌍 **Multilingual**: 3 vocabulary sizes for different use cases |
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- 📐 **LaTeX formulas**: Mathematical notation in LaTeX format |
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- 📊 **Table extraction**: Markdown table format |
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- 📝 **Document structure**: Preserves hierarchy and layout |
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- 🚀 **Production-ready**: 76.1% benchmark score, used in production |
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**Vocabulary sizes:** |
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- `151k`: Full vocabulary, all languages (default) |
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- `32k`: European languages, ~12% faster decoding |
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- `16k`: European languages, ~12% faster decoding |
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**Quick start:** |
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```bash |
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# Test on 100 samples with English text (32k vocab is fastest for European languages) |
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hf jobs uv run --flavor l4x1 \ |
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-s HF_TOKEN \ |
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https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr.py \ |
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your-input-dataset your-output-dataset \ |
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--vocab-size 32k \ |
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--batch-size 32 \ |
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--max-samples 100 |
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# Full production run on A100 (can handle huge batches!) |
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hf jobs uv run --flavor a100-large \ |
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-s HF_TOKEN \ |
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https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr.py \ |
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your-input-dataset your-output-dataset \ |
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--vocab-size 32k \ |
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--batch-size 4096 \ |
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--temperature 0.0 |
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``` |
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### DeepSeek-OCR (`deepseek-ocr-vllm.py`) ⭐ NEW |
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Advanced document OCR using [deepseek-ai/DeepSeek-OCR](https://huggingface.co/deepseek-ai/DeepSeek-OCR) with visual-text compression: |
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- 📐 **LaTeX equations** - Mathematical formulas in LaTeX format |
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- 📊 **Tables** - Extracted as HTML/markdown |
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- 📝 **Document structure** - Headers, lists, formatting preserved |
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- 🖼️ **Image grounding** - Spatial layout with bounding boxes |
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- 🔍 **Complex layouts** - Multi-column and hierarchical structures |
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- 🌍 **Multilingual** - Multiple language support |
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- 🎚️ **Resolution modes** - 5 presets for speed/quality trade-offs |
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- 💬 **Prompt modes** - 5 presets for different OCR tasks |
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- ⚡ **Fast batch processing** - vLLM acceleration |
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**Resolution Modes:** |
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- `tiny` (512×512): Fast, 64 vision tokens |
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- `small` (640×640): Balanced, 100 vision tokens |
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- `base` (1024×1024): High quality, 256 vision tokens |
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- `large` (1280×1280): Maximum quality, 400 vision tokens |
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- `gundam` (dynamic): Adaptive multi-tile (default) |
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**Prompt Modes:** |
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- `document`: Convert to markdown with grounding (default) |
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- `image`: OCR any image with grounding |
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- `free`: Fast OCR without layout |
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- `figure`: Parse figures from documents |
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- `describe`: Detailed image descriptions |
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### RolmOCR (`rolm-ocr.py`) |
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Fast general-purpose OCR using [reducto/RolmOCR](https://huggingface.co/reducto/RolmOCR) based on Qwen2.5-VL-7B: |
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- 🚀 **Fast extraction** - Optimized for speed and efficiency |
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- 📄 **Plain text output** - Clean, natural text representation |
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- 💪 **General-purpose** - Works well on various document types |
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- 🔥 **Large context** - Handles up to 16K tokens |
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- ⚡ **Batch optimized** - Efficient processing with vLLM |
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### Nanonets OCR (`nanonets-ocr.py`) |
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State-of-the-art document OCR using [nanonets/Nanonets-OCR-s](https://huggingface.co/nanonets/Nanonets-OCR-s) that handles: |
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- 📐 **LaTeX equations** - Mathematical formulas preserved |
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- 📊 **Tables** - Extracted as HTML format |
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- 📝 **Document structure** - Headers, lists, formatting maintained |
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- 🖼️ **Images** - Captions and descriptions included |
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- ☑️ **Forms** - Checkboxes rendered as ☐/☑ |
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### Nanonets OCR2 (`nanonets-ocr2.py`) |
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Next-generation Nanonets OCR using [nanonets/Nanonets-OCR2-3B](https://huggingface.co/nanonets/Nanonets-OCR2-3B) with improved accuracy: |
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- 🎯 **Enhanced quality** - 3.75B parameters for superior OCR accuracy |
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- 📐 **LaTeX equations** - Mathematical formulas preserved in LaTeX format |
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- 📊 **Advanced tables** - Improved HTML table extraction |
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- 📝 **Document structure** - Headers, lists, formatting maintained |
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- 🖼️ **Smart image captions** - Intelligent descriptions and captions |
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- ☑️ **Forms** - Checkboxes rendered as ☐/☑ |
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- 🌍 **Multilingual** - Enhanced language support |
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- 🔧 **Based on Qwen2.5-VL** - Built on state-of-the-art vision-language model |
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### SmolDocling (`smoldocling-ocr.py`) |
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Ultra-compact document understanding using [ds4sd/SmolDocling-256M-preview](https://huggingface.co/ds4sd/SmolDocling-256M-preview) with only 256M parameters: |
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- 🏷️ **DocTags format** - Efficient XML-like representation |
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- 💻 **Code blocks** - Preserves indentation and syntax |
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- 🔢 **Formulas** - Mathematical expressions with layout |
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- 📊 **Tables & charts** - Structured data extraction |
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- 📐 **Layout preservation** - Bounding boxes and spatial info |
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- ⚡ **Ultra-fast** - Tiny model size for quick inference |
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### NuMarkdown (`numarkdown-ocr.py`) |
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Advanced reasoning-based OCR using [numind/NuMarkdown-8B-Thinking](https://huggingface.co/numind/NuMarkdown-8B-Thinking) that analyzes documents before converting to markdown: |
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- 🧠 **Reasoning Process** - Thinks through document layout before generation |
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- 📊 **Complex Tables** - Superior table extraction and formatting |
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- 📐 **Mathematical Formulas** - Accurate LaTeX/math notation preservation |
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- 🔍 **Multi-column Layouts** - Handles complex document structures |
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- ✨ **Thinking Traces** - Optional inclusion of reasoning process with `--include-thinking` |
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### DoTS.ocr (`dots-ocr.py`) |
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Compact multilingual OCR using [rednote-hilab/dots.ocr](https://huggingface.co/rednote-hilab/dots.ocr) with only 1.7B parameters: |
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- 🌍 **100+ Languages** - Extensive multilingual support |
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- 📝 **Simple OCR** - Clean text extraction (default mode) |
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- 📊 **Layout Analysis** - Optional structured output with bboxes and categories |
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- 📐 **Formula recognition** - LaTeX format support |
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- 🎯 **Compact** - Only 1.7B parameters, efficient on smaller GPUs |
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- 🔀 **Flexible prompts** - Switch between OCR, layout-all, and layout-only modes |
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### olmOCR2 (`olmocr2-vllm.py`) |
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High-quality document OCR using [allenai/olmOCR-2-7B-1025-FP8](https://huggingface.co/allenai/olmOCR-2-7B-1025-FP8) optimized with GRPO reinforcement learning: |
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- 🎯 **High accuracy** - 82.4 ± 1.1 on olmOCR-Bench (84.9% on math) |
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- 📐 **LaTeX equations** - Mathematical formulas in LaTeX format |
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- 📊 **Table extraction** - Structured table recognition |
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- 📑 **Multi-column layouts** - Complex document structures |
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- 🗜️ **FP8 quantized** - Efficient 8B model for faster inference |
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- 📜 **Degraded scans** - Works well on old/historical documents |
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- 📝 **Long text extraction** - Headers, footers, and full document content |
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- 🧩 **YAML metadata** - Structured front matter (language, rotation, content type) |
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- 🚀 **Based on Qwen2.5-VL-7B** - Fine-tuned with reinforcement learning |
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## 🆕 New Features |
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### Multi-Model Comparison Support |
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All scripts now include `inference_info` tracking for comparing multiple OCR models: |
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```bash |
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# First model |
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uv run rolm-ocr.py my-dataset my-dataset --max-samples 100 |
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# Second model (appends to same dataset) |
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uv run nanonets-ocr.py my-dataset my-dataset --max-samples 100 |
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# View all models used |
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python -c "import json; from datasets import load_dataset; ds = load_dataset('my-dataset'); print(json.loads(ds[0]['inference_info']))" |
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``` |
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### Random Sampling |
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Get representative samples with the new `--shuffle` flag: |
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```bash |
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# Random 50 samples instead of first 50 |
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uv run rolm-ocr.py ordered-dataset output --max-samples 50 --shuffle |
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# Reproducible random sampling |
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uv run nanonets-ocr.py dataset output --max-samples 100 --shuffle --seed 42 |
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``` |
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### Automatic Dataset Cards |
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Every OCR run now generates comprehensive dataset documentation including: |
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- Model configuration and parameters |
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- Processing statistics |
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- Column descriptions |
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- Reproduction instructions |
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## 💻 Usage Examples |
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### Run on HuggingFace Jobs (Recommended) |
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No GPU? No problem! Run on HF infrastructure: |
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```bash |
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# DeepSeek-OCR - Real-world example (National Library of Scotland handbooks) |
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hf jobs uv run --flavor a100-large \ |
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-s HF_TOKEN \ |
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-e UV_TORCH_BACKEND=auto \ |
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https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr-vllm.py \ |
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NationalLibraryOfScotland/Britain-and-UK-Handbooks-Dataset \ |
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davanstrien/handbooks-deep-ocr \ |
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--max-samples 100 \ |
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--shuffle \ |
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--resolution-mode large |
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# DeepSeek-OCR - Fast testing with tiny mode |
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hf jobs uv run --flavor l4x1 \ |
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-s HF_TOKEN \ |
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-e UV_TORCH_BACKEND=auto \ |
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https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr-vllm.py \ |
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your-input-dataset your-output-dataset \ |
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--max-samples 10 \ |
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--resolution-mode tiny |
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# DeepSeek-OCR - Parse figures from scientific papers |
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hf jobs uv run --flavor a100-large \ |
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-s HF_TOKEN \ |
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-e UV_TORCH_BACKEND=auto \ |
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https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr-vllm.py \ |
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scientific-papers figures-extracted \ |
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--prompt-mode figure |
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# Basic OCR job with Nanonets |
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hf jobs uv run --flavor l4x1 \ |
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--secrets HF_TOKEN \ |
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https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \ |
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your-input-dataset your-output-dataset |
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# DoTS.ocr - Multilingual OCR with compact 1.7B model |
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hf jobs uv run --flavor a100-large \ |
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--secrets HF_TOKEN \ |
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https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-ocr.py \ |
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davanstrien/ufo-ColPali \ |
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your-username/ufo-ocr \ |
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--batch-size 256 \ |
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--max-samples 1000 \ |
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--shuffle |
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# Real example with UFO dataset 🛸 |
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hf jobs uv run \ |
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--flavor a10g-large \ |
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--secrets HF_TOKEN \ |
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https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \ |
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davanstrien/ufo-ColPali \ |
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your-username/ufo-ocr \ |
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--image-column image \ |
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--max-model-len 16384 \ |
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--batch-size 128 |
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# Nanonets OCR2 - Next-gen quality with 3B model |
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hf jobs uv run \ |
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--flavor l4x1 \ |
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--secrets HF_TOKEN \ |
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https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr2.py \ |
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your-input-dataset \ |
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your-output-dataset \ |
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--batch-size 16 |
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# NuMarkdown with reasoning traces for complex documents |
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hf jobs uv run \ |
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--flavor l4x4 \ |
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--secrets HF_TOKEN \ |
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https://huggingface.co/datasets/uv-scripts/ocr/raw/main/numarkdown-ocr.py \ |
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your-input-dataset your-output-dataset \ |
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--max-samples 50 \ |
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--include-thinking \ |
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--shuffle |
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# olmOCR2 - High-quality OCR with YAML metadata |
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hf jobs uv run \ |
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--flavor a100-large \ |
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--secrets HF_TOKEN \ |
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https://huggingface.co/datasets/uv-scripts/ocr/raw/main/olmocr2-vllm.py \ |
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your-input-dataset your-output-dataset \ |
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--batch-size 16 \ |
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--max-samples 100 |
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# Private dataset with custom settings |
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hf jobs uv run --flavor l40sx1 \ |
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--secrets HF_TOKEN \ |
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https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \ |
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private-input private-output \ |
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--private \ |
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--batch-size 32 |
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``` |
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### Python API |
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```python |
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from huggingface_hub import run_uv_job |
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job = run_uv_job( |
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"https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py", |
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args=["input-dataset", "output-dataset", "--batch-size", "16"], |
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flavor="l4x1" |
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) |
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``` |
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### Run Locally (Requires GPU) |
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```bash |
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# Clone and run |
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git clone https://huggingface.co/datasets/uv-scripts/ocr |
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cd ocr |
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uv run nanonets-ocr.py input-dataset output-dataset |
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# Or run directly from URL |
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uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \ |
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input-dataset output-dataset |
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# RolmOCR for fast text extraction |
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uv run rolm-ocr.py documents extracted-text |
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uv run rolm-ocr.py images texts --shuffle --max-samples 100 # Random sample |
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# Nanonets OCR2 for highest quality |
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uv run nanonets-ocr2.py documents ocr-results |
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``` |
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## 📁 Works With |
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Any HuggingFace dataset containing images - documents, forms, receipts, books, handwriting. |
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## 🎛️ Configuration Options |
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### Common Options (All Scripts) |
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| Option | Default | Description | |
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| -------------------------- | ------- | ----------------------------- | |
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| `--image-column` | `image` | Column containing images | |
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| `--batch-size` | `32`/`16`* | Images processed together | |
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| `--max-model-len` | `8192`/`16384`** | Max context length | |
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| `--max-tokens` | `4096`/`8192`** | Max output tokens | |
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| `--gpu-memory-utilization` | `0.8` | GPU memory usage (0.0-1.0) | |
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| `--split` | `train` | Dataset split to process | |
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| `--max-samples` | None | Limit samples (for testing) | |
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| `--private` | False | Make output dataset private | |
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| `--shuffle` | False | Shuffle dataset before processing | |
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| `--seed` | `42` | Random seed for shuffling | |
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*RolmOCR and DoTS use batch size 16 |
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**RolmOCR uses 16384/8192 |
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### Script-Specific Options |
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**DeepSeek-OCR**: |
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- `--resolution-mode`: Quality level - `tiny`, `small`, `base`, `large`, or `gundam` (default) |
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- `--prompt-mode`: Task type - `document` (default), `image`, `free`, `figure`, or `describe` |
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- `--prompt`: Custom OCR prompt (overrides prompt-mode) |
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- `--base-size`, `--image-size`, `--crop-mode`: Override resolution mode manually |
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- ⚠️ **Important for HF Jobs**: Add `-e UV_TORCH_BACKEND=auto` for proper PyTorch installation |
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**RolmOCR**: |
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- Output column is auto-generated from model name (e.g., `rolmocr_text`) |
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- Use `--output-column` to override the default name |
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**DoTS.ocr**: |
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- `--prompt-mode`: Choose `ocr` (default), `layout-all`, or `layout-only` |
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- `--custom-prompt`: Override with custom prompt text |
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- `--output-column`: Output column name (default: `markdown`) |
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💡 **Performance tip**: Increase batch size for faster processing (e.g., `--batch-size 256` on A100) |
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