ocr-detection 0.4.1

A Python library to detect whether PDF pages contain extractable text or are scanned images requiring OCR

OCR Detection Library

A Python library to analyze PDF pages and determine whether they contain extractable text or are scanned images requiring OCR processing.

NEW in v0.3.0: Smart Image Extraction provides 5x faster performance for scanned PDFs with 33% less memory usage! Now includes 40x faster default processing mode and optimized parallel processing for large documents.

Features

• Page Type Detection: Automatically classifies PDF pages as text, scanned, mixed, or empty
• Smart Image Extraction: 5x faster image processing for scanned PDFs using embedded images
• Base64 Image Output: Get page images as base64-encoded strings for visualization
• Dual Processing Modes: Fast mode (40x faster) for speed, accuracy mode for precision
• Parallel Processing: Fast analysis of large PDFs using multi-threading (up to 8x speedup)
• Confidence Scoring: Reliability indicators for classifications
• Memory Efficient: 33% reduction in memory usage with optimized image handling
• Simple API: Easy-to-use interface with minimal complexity

Installation

# Clone or download the project
cd ocr-detection

# Install with uv (recommended)
uv sync

# Or install with pip
pip install ocr-detection

Usage

Quick Start

from ocr_detection import detect_ocr

# RECOMMENDED: Serverless mode with images - optimal for most use cases
# (12-17s for 1000+ pages, includes optimized images for OCR processing)
result = detect_ocr("document.pdf", serverless_mode=True, include_images=True)

# RECOMMENDED: Serverless mode for classification only - ultra-fast
# (sub-2 seconds for 1000+ pages, no images)
result = detect_ocr("document.pdf", serverless_mode=True)

# Traditional fast mode - 40x faster than accuracy mode
result = detect_ocr("document.pdf")

# Accuracy mode - slowest but most precise
result = detect_ocr("document.pdf", accuracy_mode=True)

print(result)
# Output: {"status": "partial", "pages": [1, 3, 7, 12]}

# Check the status
if result['status'] == "true":
    print("All pages need OCR")
elif result['status'] == "false":
    print("No pages need OCR")
else:  # partial
    print(f"Pages needing OCR: {result['pages']}")

Recommended Usage (Serverless Optimized)

For Google Cloud Functions/Run and other serverless environments:

from ocr_detection import detect_ocr, OCRDetection

# Option 1: Quick function call with images (RECOMMENDED)
# Perfect balance of speed and functionality
result = detect_ocr("document.pdf", serverless_mode=True, include_images=True)
# Performance: 12-17s for 1000+ pages with optimized images

# Option 2: Classification only (ultra-fast)
# When you only need to know which pages need OCR
result = detect_ocr("document.pdf", serverless_mode=True)
# Performance: sub-2 seconds for 1000+ pages

# Option 3: Class-based approach
detector = OCRDetection(serverless_mode=True, include_images=True)
result = detector.detect("document.pdf")

Using the OCRDetection Class

from ocr_detection import OCRDetection

# RECOMMENDED: Serverless mode - optimal for most use cases
# Automatically enables metadata_only=True, optimized images, and conservative parallelization
serverless_detector = OCRDetection(serverless_mode=True)

# RECOMMENDED: Serverless mode with images for OCR processing
# (12-17s for 1000+ pages with optimized ultra-fast image generation)
serverless_with_images = OCRDetection(serverless_mode=True, include_images=True)

# Traditional fast mode - 40x faster than accuracy mode
detector = OCRDetection(
    accuracy_mode=False,       # Fast mode (default)
    confidence_threshold=0.5,  # Minimum confidence for OCR detection
    parallel=True,             # Enable parallel processing
    include_images=False,      # No images by default
    image_format="png",        # Image format: "png" or "jpeg"
    image_dpi=150             # Image resolution (DPI)
)

# Accuracy mode - slowest but most precise
accurate_detector = OCRDetection(accuracy_mode=True)

# Analyze a document
result = detector.detect("document.pdf")

# With custom parallel settings for large documents
result = detector.detect("large_document.pdf", parallel=True, max_workers=8)

Understanding Results

The library returns a dictionary with the following fields:

• status: Indicates the OCR requirement

  • "true" - All pages need OCR processing
  • "false" - No pages need OCR processing
  • "partial" - Some pages need OCR processing

• pages: List of page numbers (1-indexed) that need OCR processing

  • Empty list when status is "false"
  • Contains all page numbers when status is "true"
  • Contains specific page numbers when status is "partial"

• page_images: Dictionary mapping page numbers to base64-encoded images (when include_images=True)

  • Only included for pages that need OCR processing
  • Page numbers are 1-indexed to match PDF page numbering
  • Images are base64-encoded PNG or JPEG strings

Examples

from ocr_detection import detect_ocr

# Example 1: Fully text-based PDF
result = detect_ocr("text_document.pdf")
# {"status": "false", "pages": []}

# Example 2: Scanned PDF
result = detect_ocr("scanned_document.pdf")
# {"status": "true", "pages": [1, 2, 3, 4, 5]}

# Example 3: Mixed content PDF
result = detect_ocr("mixed_document.pdf")
# {"status": "partial", "pages": [2, 5, 8]}

# Example 4: With base64 images
result = detect_ocr("document.pdf", include_images=True)
# {
#   "status": "partial", 
#   "pages": [2, 5], 
#   "page_images": {
#     2: "iVBORw0KGgoAAAANSUhEUgAA...",  # base64 PNG data
#     5: "iVBORw0KGgoAAAANSUhEUgAA..."   # base64 PNG data
#   }
# }

# Example 5: Custom image settings
result = detect_ocr(
    "document.pdf", 
    include_images=True,
    image_format="jpeg",  # Use JPEG instead of PNG
    image_dpi=200        # Higher resolution
)

# Example 6: With parallel processing for large PDFs
result = detect_ocr("large_document.pdf", parallel=True, max_workers=8)

# Example 7: Accuracy vs Speed modes
fast_result = detect_ocr("document.pdf")  # Fast mode (default)
accurate_result = detect_ocr("document.pdf", accuracy_mode=True)  # Accuracy mode

# Example 8: Serverless optimization (RECOMMENDED)
serverless_result = detect_ocr("document.pdf", serverless_mode=True, include_images=True)  # Optimal balance

# Example 9: Ultra-fast classification only
classify_result = detect_ocr("document.pdf", serverless_mode=True)  # Sub-2 seconds for 1000+ pages

Image Output Options

The library can generate base64-encoded images of pages that need OCR processing:

Parameters

• include_images: bool - Enable base64 image output (default: False)
• image_format: str - Output format: "png" or "jpeg" (default: "png")
• image_dpi: int - Resolution in DPI (default: 150)

Usage Notes

• Images are only generated for pages that need OCR processing
• Smart extraction: Scanned pages use embedded images for 5x faster processing
• Higher DPI values produce larger but clearer images (only affects rendered pages)
• PNG format preserves quality but has larger file sizes
• JPEG format is more compact but may have compression artifacts
• Page numbers in page_images match those in the pages list (1-indexed)

Performance

Version 0.3.0 Optimization

The library now features Smart Image Extraction for dramatically improved performance:

• 5x faster processing for scanned PDFs (2.5s → 0.54s)
• 33% memory reduction (116MB → 79MB)
• 8x smaller image data (15.9MB → 2.0MB)
• 20x faster per-image processing (1.2s → 0.06s per image)

How It Works

• Scanned PDFs: Extracts original embedded JPEG images directly (no re-rendering)
• Text PDFs: Uses traditional rendering for vector content
• Quality Preservation: Maintains original image compression and quality
• Thread Safety: Works seamlessly with parallel processing

Processing Modes

Fast Mode (Default):

• 40x faster than accuracy mode
• Uses optimized text extraction (PyMuPDF only)
• Fast page classification heuristics
• Recommended for most use cases

Accuracy Mode:

• Maximum precision using dual text extraction
• Comprehensive text quality analysis
• Better for documents requiring high confidence
• Use when precision is more important than speed

Automatic Optimization

The library automatically optimizes performance based on document size and content:

• Documents with ≤10 pages use sequential processing
• Larger documents automatically use parallel processing
• Current parallel limit: 8 workers (configurable)
• Parallel speedup: 3-8x performance improvement for large documents
• Worker optimization: min(cpu_count, total_pages, max_workers)
• Smart image extraction eliminates unnecessary rendering overhead

Performance Tuning

# For maximum speed on large documents
result = detect_ocr(
    "large_document.pdf",
    accuracy_mode=False,    # Fast mode
    parallel=True,          # Enable parallel processing
    max_workers=8          # Use up to 8 workers
)

# For maximum accuracy
result = detect_ocr(
    "document.pdf",
    accuracy_mode=True     # Accuracy mode (slower)
)

# Custom worker count for high-core systems
result = detect_ocr(
    "huge_document.pdf",
    parallel=True,
    max_workers=16         # Increase for powerful hardware
)

Benchmark Results

Large Document Test (1045 pages, 3.9MB):

• Fast mode: ~8.0s
• Fast mode + images: ~33.7s
• Parallel processing: 3-8x faster than sequential
• Memory usage: Optimized with 33% reduction

Performance Guidelines:

• Use fast mode for general document analysis
• Use accuracy mode when precision is critical
• Parallel processing automatically enabled for >10 pages
• Increase max_workers on high-core systems for better performance

License

MIT License - see LICENSE file for details