OCR UV Scripts

Name: ocr
Creator: 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 \
    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

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 ☐/☑

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

🆕 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:

# Basic OCR job
hf jobs uv run --flavor l4x1 \
    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \
    your-input-dataset your-output-dataset


# Real example with UFO dataset 🛸
hf jobs uv run \
    --flavor a10g-large \
    --image vllm/vllm-openai:latest \
    -s HF_TOKEN=$(python3 -c "from huggingface_hub import get_token; print(get_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

# NuMarkdown with reasoning traces for complex documents
hf jobs uv run \
    --image vllm/vllm-openai:latest \
    --flavor l4x4 \
    -s HF_TOKEN=$(python3 -c "from huggingface_hub import get_token; print(get_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

# Private dataset with custom settings
hf jobs uv run --flavor l40sx1 \
    -s HF_TOKEN=$(python3 -c "from huggingface_hub import get_token; print(get_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

📁 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 uses batch size 16 **RolmOCR uses 16384/8192

RolmOCR Specific

Output column is auto-generated from model name (e.g., rolmocr_text)
Use --output-column to override the default name

💡 Performance tip: Increase batch size for faster processing (e.g., --batch-size 128 for A10G GPUs)

More OCR VLM Scripts coming soon! Stay tuned for updates!