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tags:
  - uv-script
  - ocr
  - vision-language-model
  - document-processing

OCR UV Scripts

Part of 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

Run OCR on any dataset without needing your own GPU:

# 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:

  • ⚡ 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:

# 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

Advanced document OCR using deepseek-ai/DeepSeek-OCR with visual-text compression:

  • 📐 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)

Fast general-purpose OCR using reducto/RolmOCR based on Qwen2.5-VL-7B:

  • 🚀 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)

State-of-the-art document OCR using nanonets/Nanonets-OCR-s that handles:

  • 📐 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)

Next-generation Nanonets OCR using nanonets/Nanonets-OCR2-3B with improved accuracy:

  • 🎯 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)

Ultra-compact document understanding using ds4sd/SmolDocling-256M-preview with only 256M parameters:

  • 🏷️ 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)

Advanced reasoning-based OCR using numind/NuMarkdown-8B-Thinking that analyzes documents before converting to markdown:

  • 🧠 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)

Compact multilingual OCR using rednote-hilab/dots.ocr with only 1.7B parameters:

  • 🌍 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)

High-quality document OCR using allenai/olmOCR-2-7B-1025-FP8 optimized with GRPO reinforcement learning:

  • 🎯 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

🆕 New Features

Multi-Model Comparison Support

All scripts now include inference_info tracking for comparing multiple OCR models:

# 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

Get representative samples with the new --shuffle flag:

# 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

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:

# 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 

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) 

# 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

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)