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