unifex

A Python library for document text extraction with local and cloud OCR solutions.

Focus: Built for tasks like fraud detection where precision matters. We needed a universal tool for both PDF and image processing with best-in-class OCR support through local engines (EasyOCR, Tesseract, PaddleOCR) and cloud services (Azure Document Intelligence, Google Document AI).

📖 Documentation

Features

• Multiple OCR Backends: Local (EasyOCR, Tesseract, PaddleOCR) and cloud (Azure Document Intelligence, Google Document AI) OCR support
• PDF Text Extraction: Native PDF text extraction using pypdfium2
• LLM Extraction: Extract structured data using GPT-4o, Claude, Gemini, or OpenAI-compatible APIs
• Unified Coordinates: Seamless conversion between POINTS, PIXELS, INCHES, and NORMALIZED coordinate systems
• Table Extraction: PDF (tabula), PaddleOCR (PPStructure), and cloud OCR (Azure DI, Google DocAI)
• Parallel Extraction: Process multiple pages concurrently with thread or process executors
• Async Support: Native async/await API for integration with async applications
• Unified Extractors: Each OCR extractor auto-detects file type (PDF vs image) and handles conversion internally
• Schema Adapters: Clean separation of external API schemas from internal models
• Pydantic Models: Type-safe document representation with pydantic v1/v2 compatibility

Alternatives

For broader document processing, check out Docling and Kreuzberg.

Installation

pip install unifex

Or with optional dependencies:

pip install unifex[pdf]       # PDF text extraction
pip install unifex[easyocr]   # EasyOCR support
pip install unifex[tesseract] # Tesseract OCR support
pip install unifex[azure]     # Azure Document Intelligence
pip install unifex[google]    # Google Document AI
pip install unifex[llm-openai]     # OpenAI/GPT-4 extraction
pip install unifex[llm-anthropic]  # Anthropic/Claude extraction
pip install unifex[all]       # All dependencies

Quick Start

Factory Interface (Recommended)

The simplest way to use unifex is via the factory interface. Both string paths and Path objects are accepted:

from unifex import create_extractor, ExtractorType

# PDF extraction (native text) - string path
with create_extractor("document.pdf", ExtractorType.PDF) as extractor:
    result = extractor.extract()
    doc = result.document  # Access the Document

# EasyOCR for images
with create_extractor("image.png", ExtractorType.EASYOCR, languages=["en"]) as extractor:
    result = extractor.extract()

# EasyOCR for PDFs (auto-converts to images internally)
with create_extractor("scanned.pdf", ExtractorType.EASYOCR, dpi=200) as extractor:
    result = extractor.extract()

# Azure Document Intelligence (credentials from env vars)
with create_extractor("document.pdf", ExtractorType.AZURE_DI) as extractor:
    result = extractor.extract()

# Path objects also work
from pathlib import Path
with create_extractor(Path("document.pdf"), ExtractorType.PDF) as extractor:
    result = extractor.extract()

Example Output

The extract() method returns an ExtractionResult containing the Document and per-page results:

from unifex import create_extractor, ExtractorType

with create_extractor("document.pdf", ExtractorType.PDF) as extractor:
    result = extractor.extract()

# Check extraction status
print(f"Success: {result.success}")  # True if all pages extracted

# Access extracted document
doc = result.document
print(f"Pages: {len(doc.pages)}")  # Pages: 2

for page in doc.pages:
    print(f"Page {page.page + 1} ({page.width:.0f}x{page.height:.0f}):")
    for text in page.texts:
        print(f"  - \"{text.text}\"")
        print(f"    bbox: ({text.bbox.x0:.1f}, {text.bbox.y0:.1f}, {text.bbox.x1:.1f}, {text.bbox.y1:.1f})")

# Handle errors if any
if not result.success:
    for page_num, error in result.errors:
        print(f"Page {page_num} failed: {error}")

Output:

Pages: 2
Page 1 (595x842):
  - "First page. First text"
    bbox: (48.3, 57.8, 205.4, 74.6)
  - "First page. Second text"
    bbox: (48.0, 81.4, 231.2, 98.6)
  - "First page. Fourth text"
    bbox: (47.8, 120.5, 221.9, 137.4)
Page 2 (595x842):
  - "Second page. Third text"
    bbox: (47.4, 81.1, 236.9, 98.3)

For more detailed examples, see the documentation.

PDF Text Extraction

from unifex import PdfExtractor

# String paths work directly
with PdfExtractor("document.pdf") as extractor:
    result = extractor.extract()
    for page in result.document.pages:
        for text in page.texts:
            print(text.text)

Language Codes

All OCR extractors use 2-letter ISO 639-1 language codes (e.g., "en", "fr", "de", "it"). Extractors that require different formats (like Tesseract) convert internally.

Parallel Extraction

Extract multiple pages concurrently for faster processing:

from unifex import create_extractor, ExtractorType, ExecutorType

# Thread-based parallelism (recommended for most cases)
with create_extractor("large_document.pdf", ExtractorType.EASYOCR) as extractor:
    result = extractor.extract(max_workers=4)  # 4 parallel workers

# Process-based parallelism (for CPU-bound pure Python workloads)
with create_extractor("large_document.pdf", ExtractorType.EASYOCR) as extractor:
    result = extractor.extract(max_workers=4, executor=ExecutorType.PROCESS)

# Extract specific pages in parallel
with create_extractor("document.pdf", ExtractorType.PDF) as extractor:
    result = extractor.extract(pages=[0, 2, 5, 8], max_workers=4)

Executor Types:

Executor	Best For	Notes
THREAD (default)	Most OCR use cases	Shared model cache, low overhead, C libraries release GIL
PROCESS	CPU-bound pure Python	Models duplicated per worker, higher memory usage

Async Extraction

For async applications, use the async API:

import asyncio
from unifex import create_extractor, ExtractorType

async def extract_document():
    with create_extractor("document.pdf", ExtractorType.EASYOCR) as extractor:
        result = await extractor.extract_async(max_workers=4)
        return result.document

doc = asyncio.run(extract_document())

OCR Extraction

Local OCR engines (EasyOCR, Tesseract, PaddleOCR) and cloud services (Azure Document Intelligence, Google Document AI). All extractors auto-detect file type (PDF vs image) and handle conversion internally.

from unifex import (
    EasyOcrExtractor, TesseractOcrExtractor, PaddleOcrExtractor,
    AzureDocumentIntelligenceExtractor, GoogleDocumentAIExtractor,
)

# Local OCR (works for both images and PDFs)
with EasyOcrExtractor("scanned.pdf", languages=["en"], dpi=200) as extractor:
    result = extractor.extract()

# Tesseract (requires system install: brew install tesseract)
with TesseractOcrExtractor("image.png", languages=["en"]) as extractor:
    result = extractor.extract()

# PaddleOCR (excellent for Chinese)
with PaddleOcrExtractor("chinese_doc.png", lang="ch") as extractor:
    result = extractor.extract()

# Cloud: Azure Document Intelligence
with AzureDocumentIntelligenceExtractor(
    "document.pdf",
    endpoint="https://your-resource.cognitiveservices.azure.com",
    key="your-api-key",
) as extractor:
    result = extractor.extract()

# Cloud: Google Document AI
with GoogleDocumentAIExtractor(
    "document.pdf",
    processor_name="projects/your-project/locations/us/processors/id",
    credentials_path="/path/to/credentials.json",
) as extractor:
    result = extractor.extract()

LLM Extraction

Extract structured data using vision-capable LLMs (OpenAI, Anthropic, Google, Azure OpenAI). Supports custom prompts, Pydantic schemas, parallel extraction, async API, and OpenAI-compatible endpoints (vLLM, Ollama).

from pydantic import BaseModel
from unifex.llm import extract_structured, extract_structured_async

class Invoice(BaseModel):
    invoice_number: str
    date: str
    total: float

# Basic extraction with Pydantic schema
result = extract_structured("invoice.pdf", model="openai/gpt-4o", schema=Invoice)
invoice: Invoice = result.data

# With custom prompt and parallel workers
result = extract_structured(
    "large_doc.pdf",
    model="anthropic/claude-sonnet-4-20250514",
    prompt="Extract invoice details",
    max_workers=4,  # Process pages in parallel
)

# OpenAI-compatible APIs (vLLM, Ollama) with custom headers
result = extract_structured(
    "document.pdf",
    model="openai/llava",
    base_url="http://localhost:11434/v1",
    headers={"X-Custom-Auth": "token"},
)

# Async API
result = await extract_structured_async("doc.pdf", model="openai/gpt-4o", max_workers=4)

CLI Usage

# OCR extractors: pdf, easyocr, tesseract, paddle, azure-di, google-docai
uv run python -m unifex.cli document.pdf --extractor easyocr --lang en

# Parallel extraction with process executor
uv run python -m unifex.cli large_doc.pdf --extractor easyocr --max-workers 4 --executor process

# Cloud OCR (credentials via CLI or env vars)
uv run python -m unifex.cli document.pdf --extractor azure-di \
    --azure-endpoint https://your-resource.cognitiveservices.azure.com --azure-key your-key

# LLM extraction with parallel workers and custom endpoint
uv run python -m unifex.cli document.pdf --llm openai/gpt-4o --max-workers 4 \
    --llm-base-url https://your-proxy.com/v1 --llm-header "X-Auth=token"

# JSON output, specific pages
uv run python -m unifex.cli document.pdf --extractor pdf --pages 0,1,2 --json

Environment Variables

Cloud extractors and LLM providers support configuration via environment variables:

OCR Extractors:

Variable	Description
UNIFEX_AZURE_DI_ENDPOINT	Azure Document Intelligence endpoint URL
UNIFEX_AZURE_DI_KEY	Azure Document Intelligence API key
UNIFEX_AZURE_DI_MODEL	Azure model ID (default: prebuilt-read)
UNIFEX_GOOGLE_DOCAI_PROCESSOR_NAME	Google Document AI processor name
UNIFEX_GOOGLE_DOCAI_CREDENTIALS_PATH	Path to Google service account JSON

LLM Providers:

Variable	Description
OPENAI_API_KEY	OpenAI API key
ANTHROPIC_API_KEY	Anthropic API key
GOOGLE_API_KEY	Google AI API key
AZURE_OPENAI_API_KEY	Azure OpenAI API key
AZURE_OPENAI_ENDPOINT	Azure OpenAI endpoint URL
AZURE_OPENAI_API_VERSION	Azure OpenAI API version (default: 2024-02-15-preview)

Development

Setup

# Install dependencies
uv sync

# Install pre-commit hooks
uv run pre-commit install

Running Tests

# Run all tests
uv run pytest

# Run fast tests only (unit tests, <0.5s per test)
uv run pytest tests/base tests/ocr

# Run integration tests only (slow, load ML models)
uv run pytest tests/integration

# Run with coverage
uv run pytest --cov=unifex --cov-report=term-missing

Test Structure

tests/
├── base/           # Fast unit tests (<0.5s each) - run in pre-commit
├── ocr/            # OCR adapter unit tests (mocked) - run in pre-commit
├── llm/            # LLM unit tests (mocked) - run in pre-commit
└── integration/    # Slow tests - NOT in pre-commit
    ├── ocr/        # OCR integration tests (load real ML models)
    └── llm/        # LLM integration tests (call real APIs)

Pre-commit runs: tests/base, tests/ocr, and tests/llm with 0.5s timeout per test.

CI runs: All tests including integration tests.

Integration Tests

Integration tests load real ML models and call real services. They are in tests/integration/.

Local extractors (no credentials required):

• PdfExtractor - Tests PDF text extraction
• EasyOcrExtractor - Tests image and PDF OCR with EasyOCR
• TesseractOcrExtractor - Tests image and PDF OCR with Tesseract (requires Tesseract installed)
• PaddleOcrExtractor - Tests image and PDF OCR with PaddleOCR

Cloud extractors (require credentials):

• AzureDocumentIntelligenceExtractor - Tests Azure Document Intelligence
• GoogleDocumentAIExtractor - Tests Google Document AI

Azure Credentials Setup

1. Copy the example environment file:

   cp .env.example .env
   

2. Edit .env with your Azure Document Intelligence credentials:

   UNIFEX_AZURE_DI_ENDPOINT=https://your-resource.cognitiveservices.azure.com
   UNIFEX_AZURE_DI_KEY=your-api-key
   

3. Load environment variables and run tests:

   set -a; source .env; set +a;
   uv run pytest tests/test_integration.py -v
   

Azure integration tests are automatically skipped if credentials are not configured.

Google Document AI Credentials Setup

1. Create a Google Cloud project and enable the Document AI API

2. Create a Document AI processor in the Google Cloud Console

3. Create a service account with Document AI permissions

4. Download the service account JSON key file

5. Edit .env with your Google Document AI credentials:

   UNIFEX_GOOGLE_DOCAI_PROCESSOR_NAME=projects/your-project/locations/us/processors/your-processor-id
   UNIFEX_GOOGLE_DOCAI_CREDENTIALS_PATH=/path/to/your/service-account.json
   

Google Document AI integration tests are automatically skipped if credentials are not configured.

Documentation

Build and serve the documentation locally:

# Serve docs with live reload
uv run mkdocs serve

# Build static site
uv run mkdocs build

Open http://localhost:8000 to view the documentation.

Pre-commit Checks

The pre-commit hook runs automatically on git commit. To run manually:

uv run pre-commit run --all-files

This runs:

• ruff format - Code formatting
• ruff check --fix - Linting with auto-fix
• ty check - Type checking
• pytest - Test suite

License

BSD 3-Clause License. See LICENSE for details.

Future plans

• Detecting language helper
• Performance measurement