PDF OCR Confidence

Optimize your Docling pipeline at scale — Pre-filter PDFs to skip unnecessary OCR processing and route documents to the right backend.

What It Does

Analyzes PDF documents before expensive OCR processing to:

• ✅ Skip Docling entirely for native text PDFs (10x faster)
• ✅ Route to optimal OCR backend (Tesseract vs EasyOCR)
• ✅ Batch triage 10K+ PDFs into priority queues
• ✅ Estimate processing time/cost before committing resources

Why Use This?

Problem: Docling initialization takes 2-3 seconds per PDF. For native text documents, that's pure overhead.

Solution: Pre-analyze PDFs in milliseconds, extract native text directly, and only use Docling when necessary.

Performance Gains

Processing 1000 mixed-quality PDFs:

Method	Time	Cost
Without pre-filtering	50 min	100%
With pre-filtering	23 min	54% faster ✅

Assumes 60% native text, 30% high-quality scans, 10% low-quality scans

Installation

pip install pdf-ocr-confidence

Quick Start: Docling Integration

1. Fast-path for Native Text PDFs

from pdf_ocr_confidence import should_use_docling, extract_native_text

if not should_use_docling("report.pdf"):
    # Skip Docling - extract text directly (10x faster)
    text = extract_native_text("report.pdf")
else:
    # Use Docling for scanned/low-quality PDFs
    from docling.document_converter import DocumentConverter
    doc = DocumentConverter().convert("report.pdf")
    text = doc.export_to_markdown()

2. Batch Triage & Optimal Routing

from pdf_ocr_confidence import get_docling_strategy

# Analyze each PDF
strategy = get_docling_strategy("invoice.pdf")

if not strategy["use_docling"]:
    # Native text - skip Docling
    text = extract_native_text("invoice.pdf")
elif strategy["ocr_backend"] == "tesseract":
    # High quality - use fast OCR
    doc = DocumentConverter(
        pipeline_options=PipelineOptions(
            do_ocr=True,
            ocr_backend="tesseract"
        )
    ).convert("invoice.pdf")
else:
    # Low quality - use robust OCR
    doc = DocumentConverter(
        pipeline_options=PipelineOptions(
            do_ocr=True,
            ocr_backend="easyocr"
        )
    ).convert("invoice.pdf")

3. Cost Estimation Before Processing

from pdf_ocr_confidence import estimate_processing_time

estimate = estimate_processing_time("large_doc.pdf")
print(f"Expected time: {estimate['time_seconds']}s")
print(f"Recommended backend: {estimate['recommended_backend']}")
print(f"Page count: {estimate['page_count']}")

Advanced Usage

Standalone Analysis (Without Docling)

from pdf_ocr_confidence import analyze_pdf

result = analyze_pdf("document.pdf")

print(f"Confidence: {result.confidence:.2f}")
print(f"Recommendation: {result.recommendation}")

# Per-page details
for page in result.pages:
    print(f"Page {page.number}: {page.confidence:.2f}")

Custom Thresholds

from pdf_ocr_confidence import analyze_pdf, ConfidenceConfig

config = ConfidenceConfig(
    expensive_ocr_threshold=0.4,  # Below this = expensive OCR
    cheap_ocr_threshold=0.7,      # Above this = cheap OCR
    sample_pages=5,               # Pages to analyze (None = all)
    min_dpi=150,                  # Minimum acceptable DPI
)

result = analyze_pdf("document.pdf", config=config)

How It Works

1. Native Text Detection: Checks if PDF has extractable text layer
2. Image Quality Analysis:
   • DPI/Resolution check
   • Blur detection (Laplacian variance)
   • Contrast analysis (histogram)
   • Edge density (text clarity)
3. Confidence Scoring: Weighted combination of metrics
4. Routing Recommendation: Native text / Tesseract / EasyOCR

Real-World Example

from pdf_ocr_confidence import get_docling_strategy, extract_native_text

def process_pdf_batch(pdf_paths):
    native_count = 0
    docling_count = 0
    
    for pdf_path in pdf_paths:
        strategy = get_docling_strategy(pdf_path)
        
        if not strategy["use_docling"]:
            # Fast path: skip Docling
            text = extract_native_text(pdf_path)
            native_count += 1
        else:
            # Use Docling with optimal backend
            doc = DocumentConverter().convert(pdf_path)
            text = doc.export_to_markdown()
            docling_count += 1
    
    print(f"Processed {native_count} PDFs without Docling (fast)")
    print(f"Processed {docling_count} PDFs with Docling (OCR)")

Result: If 60% of your PDFs have native text, you save 60% of Docling initialization overhead.

Examples

See examples/docling_integration.py for:

• Complete pipeline integration
• Batch processing with queues
• Cost estimation
• Priority-based routing

Run it:

python examples/docling_integration.py your_document.pdf

Use Cases

✅ Large-scale document processing (thousands of PDFs)
✅ Mixed-quality document pipelines (invoices, reports, scans)
✅ Cost optimization for cloud OCR services
✅ Pre-filtering before Docling/Tesseract/EasyOCR
✅ Queue-based batch processing

❌ Small batches (< 100 PDFs) — overhead not worth it
❌ All scanned documents — if everything needs OCR, skip pre-analysis
❌ Single OCR backend — if you only use Tesseract, limited benefit

Performance

Operation	Time (avg)
Native text detection	0.05s per page
Quality analysis	0.1s per page
Docling initialization	2-3s per PDF

Breakeven point: If >30% of your PDFs have native text, pre-filtering is worth it.

Dependencies

• PyMuPDF (fitz) - PDF text extraction and rendering
• Pillow - Image processing
• numpy - Numerical operations
• opencv-python - Advanced image analysis

Roadmap

• Batch API for parallel analysis
• Rotation/skew detection
• Language detection for OCR model selection
• Table/form detection heuristics
• Integration examples for AWS Textract, Google Vision

Contributing

PRs welcome! Focus areas:

• Better quality heuristics
• Docling integration patterns
• Performance benchmarks
• Real-world use cases

License

MIT

Links

• PyPI: https://pypi.org/project/pdf-ocr-confidence/
• GitHub: (coming soon)
• Docling: https://github.com/DS4SD/docling