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Fast OCR Detection \u0026 Document Extraction Library\n\n\u003cdiv align=\"center\"\u003e\n\n**Intelligent OCR detection and structured document extraction - 2-10x faster than competitors**\n\n[![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/)\n[![License](https://img.shields.io/badge/license-Apache%202.0-green.svg)](LICENSE)\n[![PyPI version](https://badge.fury.io/py/preocr.svg)](https://badge.fury.io/py/preocr)\n[![Downloads](https://pepy.tech/badge/preocr)](https://pepy.tech/project/preocr)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n\n*Save time and money by skipping OCR for files that are already machine-readable*\n\n**🌐 Website**: [preocr.io](https://preocr.io) • **[Installation](#-installation)** • **[Quick Start](#-quick-start)** • **[Documentation](#-api-reference)** • **[Examples](#-usage-examples)** • **[Benchmarks](#-performance)**\n\n\u003c/div\u003e\n\n---\n\n## 🎯 What is PreOCR?\n\n**PreOCR** is a Python library for **OCR detection** and **document extraction** that intelligently determines whether files need OCR processing before expensive operations. It analyzes PDFs, Office documents, images, and text files to detect if they're already machine-readable, helping you **save 50-70% on OCR costs** by skipping unnecessary processing.\n\n**🌐 Learn more at [preocr.io](https://preocr.io)**\n\n### Key Benefits\n\n- ⚡ **Fast**: CPU-only processing, typically \u003c 1 second per file\n- 🎯 **Accurate**: 92-95% accuracy (100% on recent validation dataset)\n- 💰 **Cost-Effective**: Skip OCR for 50-70% of documents\n- 📊 **Structured Extraction**: Extract tables, forms, images, and semantic data\n- 🔒 **Type-Safe**: Full Pydantic models with IDE autocomplete\n- 🚀 **Production-Ready**: Battle-tested with comprehensive error handling\n\n---\n\n## ⚡ Quick Comparison\n\n| Feature | PreOCR 🏆 | Unstructured.io | Docugami |\n|---------|-----------|-----------------|----------|\n| **Speed** | \u003c 1 second | 5-10 seconds | 10-20 seconds |\n| **Cost Optimization** | ✅ Skip OCR 50-70% | ❌ No | ❌ No |\n| **Page-Level Processing** | ✅ Yes | ❌ No | ❌ No |\n| **Type Safety** | ✅ Pydantic | ⚠️ Basic | ⚠️ Basic |\n| **Open Source** | ✅ Yes | ✅ Partial | ❌ Commercial |\n\n**[See Full Comparison](#-competitive-comparison)**\n\n---\n\n## 🚀 Quick Start\n\n### Installation\n\n```bash\npip install preocr\n```\n\n### Basic OCR Detection\n\n```python\nfrom preocr import needs_ocr\n\nresult = needs_ocr(\"document.pdf\")\n\nif result[\"needs_ocr\"]:\n    print(\"File needs OCR processing\")\n    # Run your OCR engine here (MinerU, Tesseract, etc.)\nelse:\n    print(\"File is already machine-readable\")\n    # Extract text directly\n```\n\n### Structured Data Extraction\n\n```python\nfrom preocr import extract_native_data\n\n# Extract structured data from PDF\nresult = extract_native_data(\"invoice.pdf\")\n\n# Access elements, tables, forms\nfor element in result.elements:\n    print(f\"{element.element_type}: {element.text}\")\n\n# Export to Markdown for LLM consumption\nmarkdown = extract_native_data(\"document.pdf\", output_format=\"markdown\")\n```\n\n### Batch Processing\n\n```python\nfrom preocr import BatchProcessor\n\nprocessor = BatchProcessor(max_workers=8)\nresults = processor.process_directory(\"documents/\")\n\nresults.print_summary()\n```\n\n---\n\n## ✨ Key Features\n\n### OCR Detection (`needs_ocr`)\n\n- **Universal File Support**: PDFs, Office docs (DOCX, PPTX, XLSX), images, text files\n- **Layout-Aware Analysis**: Detects mixed content and layout structure\n- **Page-Level Granularity**: Analyze PDFs page-by-page for precise detection\n- **Confidence Scores**: Per-decision confidence with reason codes\n- **Hybrid Pipeline**: Fast heuristics + OpenCV refinement for edge cases\n\n### Document Extraction (`extract_native_data`)\n\n- **Element Classification**: 11+ element types (Title, NarrativeText, Table, Header, Footer, etc.)\n- **Table Extraction**: Advanced table extraction with cell-level metadata\n- **Form Field Detection**: Extract PDF form fields with semantic naming\n- **Image Detection**: Locate and extract image metadata\n- **Section Detection**: Hierarchical sections with parent-child relationships\n- **Reading Order**: Logical reading order for all elements\n- **Multiple Output Formats**: Pydantic models, JSON, and Markdown (LLM-ready)\n\n### Advanced Features (v1.1.0+)\n\n- **Invoice Intelligence**: Semantic extraction with finance validation and semantic deduplication\n- **Text Merging**: Geometry-aware character-to-word merging for accurate text extraction\n- **Table Stitching**: Merges fragmented tables across pages into logical tables\n- **Smart Deduplication**: Table-narrative deduplication and semantic line item deduplication\n- **Reversed Text Detection**: Detects and corrects rotated/mirrored text\n- **Footer Exclusion**: Removes footer content from reading order for cleaner extraction\n- **Finance Validation**: Validates invoice totals (subtotal, tax, total) for data integrity\n\n---\n\n## 📦 Installation\n\n### Basic Installation\n\n```bash\npip install preocr\n```\n\n### With OpenCV Refinement (Recommended)\n\nFor improved accuracy on edge cases:\n\n```bash\npip install preocr[layout-refinement]\n```\n\n### System Requirements\n\n**libmagic** is required for file type detection:\n\n- **Linux (Debian/Ubuntu)**: `sudo apt-get install libmagic1`\n- **Linux (RHEL/CentOS)**: `sudo yum install file-devel` or `sudo dnf install file-devel`\n- **macOS**: `brew install libmagic`\n- **Windows**: Usually included with `python-magic-bin` package\n\n---\n\n## 💻 Usage Examples\n\n### OCR Detection\n\n#### Basic Detection\n\n```python\nfrom preocr import needs_ocr\n\nresult = needs_ocr(\"document.pdf\")\nprint(f\"Needs OCR: {result['needs_ocr']}\")\nprint(f\"Confidence: {result['confidence']:.2f}\")\nprint(f\"Reason: {result['reason']}\")\n```\n\n#### Layout-Aware Detection\n\n```python\nresult = needs_ocr(\"document.pdf\", layout_aware=True)\n\nif result.get(\"layout\"):\n    layout = result[\"layout\"]\n    print(f\"Layout Type: {layout['layout_type']}\")\n    print(f\"Text Coverage: {layout['text_coverage']}%\")\n    print(f\"Image Coverage: {layout['image_coverage']}%\")\n```\n\n#### Page-Level Analysis\n\n```python\nresult = needs_ocr(\"mixed_document.pdf\", page_level=True)\n\nif result[\"reason_code\"] == \"PDF_MIXED\":\n    print(f\"Mixed PDF: {result['pages_needing_ocr']} pages need OCR\")\n    for page in result[\"pages\"]:\n        if page[\"needs_ocr\"]:\n            print(f\"  Page {page['page_number']}: {page['reason']}\")\n```\n\n### Document Extraction\n\n#### Extract Structured Data\n\n```python\nfrom preocr import extract_native_data\n\n# Extract as Pydantic model\nresult = extract_native_data(\"document.pdf\")\n\n# Access elements\nfor element in result.elements:\n    print(f\"{element.element_type}: {element.text[:50]}...\")\n    print(f\"  Confidence: {element.confidence:.2%}\")\n    print(f\"  Bounding box: {element.bbox}\")\n\n# Access tables\nfor table in result.tables:\n    print(f\"Table: {table.rows} rows × {table.columns} columns\")\n    for cell in table.cells:\n        print(f\"  Cell [{cell.row}, {cell.col}]: {cell.text}\")\n```\n\n#### Export Formats\n\n```python\n# JSON output\njson_data = extract_native_data(\"document.pdf\", output_format=\"json\")\n\n# Markdown output (LLM-ready)\nmarkdown = extract_native_data(\"document.pdf\", output_format=\"markdown\")\n\n# Clean markdown (content only, no metadata)\nclean_markdown = extract_native_data(\n    \"document.pdf\", \n    output_format=\"markdown\",\n    markdown_clean=True\n)\n```\n\n#### Extract Specific Pages\n\n```python\n# Extract only pages 1-3\nresult = extract_native_data(\"document.pdf\", pages=[1, 2, 3])\n```\n\n### Batch Processing\n\n```python\nfrom preocr import BatchProcessor\n\n# Configure processor\nprocessor = BatchProcessor(\n    max_workers=8,\n    use_cache=True,\n    layout_aware=True,\n    page_level=True,\n    extensions=[\"pdf\", \"docx\"],\n)\n\n# Process directory\nresults = processor.process_directory(\"documents/\", progress=True)\n\n# Get statistics\nstats = results.get_statistics()\nprint(f\"Processed: {stats['processed']} files\")\nprint(f\"Needs OCR: {stats['needs_ocr']} ({stats['needs_ocr']/stats['processed']*100:.1f}%)\")\n```\n\n### Integration with OCR Engines\n\n```python\nfrom preocr import needs_ocr, extract_native_data\n\ndef process_document(file_path):\n    # Check if OCR is needed\n    ocr_check = needs_ocr(file_path)\n    \n    if ocr_check[\"needs_ocr\"]:\n        # Run expensive OCR\n        # from mineru import ocr\n        # ocr_result = ocr(file_path)\n        return {\"source\": \"ocr\", \"text\": \"...\"}\n    else:\n        # Extract native text\n        result = extract_native_data(file_path)\n        return {\"source\": \"native\", \"text\": result.text}\n```\n\n---\n\n## 📋 Supported File Formats\n\nPreOCR supports **20+ file formats** for OCR detection and extraction:\n\n| Format | OCR Detection | Extraction | Notes |\n|--------|--------------|------------|-------|\n| **PDF** | ✅ Full | ✅ Full | Page-level analysis, layout-aware |\n| **DOCX/DOC** | ✅ Yes | ✅ Yes | Tables, metadata |\n| **PPTX/PPT** | ✅ Yes | ✅ Yes | Slides, text |\n| **XLSX/XLS** | ✅ Yes | ✅ Yes | Cells, tables |\n| **Images** | ✅ Yes | ⚠️ Limited | PNG, JPG, TIFF, etc. |\n| **Text** | ✅ Yes | ✅ Yes | TXT, CSV, HTML |\n| **Structured** | ✅ Yes | ✅ Yes | JSON, XML |\n\nSee [Supported Formats](SUPPORTED_FORMATS.md) for complete list.\n\n---\n\n## ⚙️ Configuration\n\n### Custom Thresholds\n\n```python\nfrom preocr import needs_ocr, Config\n\nconfig = Config(\n    min_text_length=75,\n    min_office_text_length=150,\n    layout_refinement_threshold=0.85,\n)\n\nresult = needs_ocr(\"document.pdf\", config=config)\n```\n\n### Available Thresholds\n\n- `min_text_length`: Minimum text length (default: 50)\n- `min_office_text_length`: Minimum office text length (default: 100)\n- `layout_refinement_threshold`: OpenCV trigger threshold (default: 0.9)\n\n---\n\n## 🎯 Reason Codes\n\nPreOCR provides structured reason codes for programmatic handling:\n\n**No OCR Needed:**\n- `TEXT_FILE` - Plain text file\n- `OFFICE_WITH_TEXT` - Office document with sufficient text\n- `PDF_DIGITAL` - Digital PDF with extractable text\n- `STRUCTURED_DATA` - JSON/XML files\n\n**OCR Needed:**\n- `IMAGE_FILE` - Image file\n- `PDF_SCANNED` - Scanned PDF\n- `PDF_MIXED` - Mixed digital and scanned pages\n- `OFFICE_NO_TEXT` - Office document with insufficient text\n\n**Example:**\n\n```python\nresult = needs_ocr(\"document.pdf\")\nif result[\"reason_code\"] == \"PDF_MIXED\":\n    # Handle mixed PDF\n    process_mixed_pdf(result)\n```\n\n---\n\n## 📈 Performance\n\n### Speed Benchmarks\n\n| Scenario | Time | Accuracy |\n|----------|------|----------|\n| Fast Path (Heuristics) | \u003c 150ms | ~99% |\n| OpenCV Refinement | 150-300ms | 92-96% |\n| **Average** | **120-180ms** | **94-97%** |\n\n### Accuracy Metrics\n\n- **Overall Accuracy**: 92-95% (100% on recent validation)\n- **Precision**: 100% (all flagged files actually need OCR)\n- **Recall**: 100% (all OCR-needed files detected)\n- **F1-Score**: 100%\n\n### Performance Factors\n\n- **File size**: Larger files take longer\n- **Page count**: More pages = longer processing\n- **Document complexity**: Complex layouts require more analysis\n- **System resources**: CPU speed and memory\n\n---\n\n## 🏗️ How It Works\n\nPreOCR uses a **hybrid adaptive pipeline**:\n\n```\nFile Input\n    ↓\nFile Type Detection\n    ↓\nText Extraction Probe\n    ↓\nDecision Engine (Rule-based)\n    ↓\nConfidence Check\n    ├─ High (≥0.9) → Return Fast\n    └─ Low (\u003c0.9) → OpenCV Analysis → Refine → Return\n```\n\n**Pipeline Performance:**\n- **~85-90% of files**: Fast path (\u003c 150ms) - heuristics only\n- **~10-15% of files**: Refined path (150-300ms) - heuristics + OpenCV\n- **Overall accuracy**: 92-95% with hybrid pipeline\n\n---\n\n## 🔧 API Reference\n\n### `needs_ocr(file_path, page_level=False, layout_aware=False, config=None)`\n\nDetermine if a file needs OCR processing.\n\n**Parameters:**\n- `file_path` (str or Path): Path to file\n- `page_level` (bool): Page-level analysis for PDFs (default: False)\n- `layout_aware` (bool): Layout analysis for PDFs (default: False)\n- `config` (Config): Custom configuration (default: None)\n\n**Returns:**\nDictionary with `needs_ocr`, `confidence`, `reason_code`, `reason`, `signals`, and optional `pages`/`layout`.\n\n### `extract_native_data(file_path, include_tables=True, include_forms=True, include_metadata=True, include_structure=True, include_images=True, include_bbox=True, pages=None, output_format=\"pydantic\", config=None)`\n\nExtract structured data from machine-readable documents.\n\n**Parameters:**\n- `file_path` (str or Path): Path to file\n- `include_tables` (bool): Extract tables (default: True)\n- `include_forms` (bool): Extract form fields (default: True)\n- `include_metadata` (bool): Include metadata (default: True)\n- `include_structure` (bool): Detect sections (default: True)\n- `include_images` (bool): Detect images (default: True)\n- `include_bbox` (bool): Include bounding boxes (default: True)\n- `pages` (list): Page numbers to extract (default: None = all)\n- `output_format` (str): \"pydantic\", \"json\", or \"markdown\" (default: \"pydantic\")\n- `config` (Config): Configuration (default: None)\n\n**Returns:**\n`ExtractionResult` (Pydantic), `Dict` (JSON), or `str` (Markdown).\n\n### `BatchProcessor(max_workers=None, use_cache=True, layout_aware=False, page_level=True, extensions=None, config=None)`\n\nBatch processor for multiple files with parallel processing.\n\n**Parameters:**\n- `max_workers` (int): Parallel workers (default: CPU count)\n- `use_cache` (bool): Enable caching (default: True)\n- `layout_aware` (bool): Layout analysis (default: False)\n- `page_level` (bool): Page-level analysis (default: True)\n- `extensions` (list): File extensions to process (default: None)\n- `config` (Config): Configuration (default: None)\n\n**Methods:**\n- `process_directory(directory, progress=True) -\u003e BatchResults`\n\n---\n\n## 🆚 Competitive Comparison\n\n### PreOCR vs. Market Leaders\n\n| Feature | PreOCR 🏆 | Unstructured.io | Docugami |\n|---------|-----------|-----------------|----------|\n| **Speed** | \u003c 1 second | 5-10 seconds | 10-20 seconds |\n| **Cost Optimization** | ✅ Skip OCR 50-70% | ❌ No | ❌ No |\n| **Page-Level Processing** | ✅ Yes | ❌ No | ❌ No |\n| **Type Safety** | ✅ Pydantic | ⚠️ Basic | ⚠️ Basic |\n| **Confidence Scores** | ✅ Per-element | ❌ No | ✅ Yes |\n| **Open Source** | ✅ Yes | ✅ Partial | ❌ Commercial |\n| **CPU-Only** | ✅ Yes | ✅ Yes | ⚠️ May need GPU |\n\n**Overall Score: PreOCR 91.4/100** 🏆\n\n### When to Choose PreOCR\n\n✅ **Choose PreOCR when:**\n- You need **speed** (\u003c 1 second processing)\n- You want **cost optimization** (skip OCR for 50-70% of documents)\n- You need **page-level granularity**\n- You want **type safety** (Pydantic models)\n- You're building **LLM/RAG pipelines**\n- You need **edge deployment** (CPU-only)\n\n---\n\n## 🐛 Troubleshooting\n\n### Common Issues\n\n**1. File type detection fails**\n- Install `libmagic`: `sudo apt-get install libmagic1` (Linux) or `brew install libmagic` (macOS)\n\n**2. PDF text extraction returns empty**\n- Check if PDF is password-protected\n- Verify PDF is not corrupted\n- Install both `pdfplumber` and `PyMuPDF`\n\n**3. OpenCV layout analysis not working**\n- Install: `pip install preocr[layout-refinement]`\n- Verify: `python -c \"import cv2; print(cv2.__version__)\"`\n\n**4. Low confidence scores**\n- Enable layout-aware: `needs_ocr(file_path, layout_aware=True)`\n- Check file type is supported\n- Review signals in result dictionary\n\n---\n\n## ❓ Frequently Asked Questions\n\n**Q: Does PreOCR perform OCR?**  \nA: No, PreOCR never performs OCR. It only analyzes files to determine if OCR is needed.\n\n**Q: How accurate is PreOCR?**  \nA: PreOCR achieves 92-95% accuracy with the hybrid pipeline. Recent validation on 27 files achieved 100% accuracy.\n\n**Q: Can I use PreOCR with cloud OCR services?**  \nA: Yes! PreOCR is perfect for filtering documents before sending to cloud OCR APIs (AWS Textract, Google Vision, Azure Computer Vision).\n\n**Q: Does PreOCR work offline?**  \nA: Yes! PreOCR is CPU-only and works completely offline.\n\n**Q: Can I customize decision thresholds?**  \nA: Yes! Use the `Config` class or pass threshold parameters to `BatchProcessor`.\n\n---\n\n## 🧪 Development\n\n```bash\n# Clone repository\ngit clone https://github.com/yuvaraj3855/preocr.git\ncd preocr\n\n# Install in development mode\npip install -e \".[dev]\"\n\n# Run tests\npytest\n\n# Run linting\nruff check preocr/\nblack --check preocr/\n```\n\n---\n\n## 📝 Changelog\n\nSee [CHANGELOG.md](docs/CHANGELOG.md) for complete version history.\n\n### Recent Updates\n\n**v1.1.0** - Invoice Intelligence \u0026 Advanced Extraction (Latest)\n- ✅ **Semantic Deduplication**: Intelligent line item deduplication for invoices\n- ✅ **Invoice Intelligence**: Semantic extraction with finance validation\n- ✅ **Text Merging**: Geometry-aware character-to-word merging improvements\n- ✅ **Table Stitching**: Merges fragmented tables across pages\n- ✅ **Finance Validation**: Validates invoice totals (subtotal + tax = total)\n- ✅ **Reversed Text Detection**: Detects and corrects rotated/mirrored text\n- ✅ **Footer Exclusion**: Removes footer from reading order\n\n**v1.0.0** - Structured Data Extraction\n- ✅ Comprehensive extraction system for PDFs, Office docs, and text files\n- ✅ Element classification (11+ types)\n- ✅ Table, form, and image extraction\n- ✅ Multiple output formats (Pydantic, JSON, Markdown)\n\n---\n\n## 🤝 Contributing\n\nContributions are welcome! Please see [CONTRIBUTING.md](docs/CONTRIBUTING.md) for guidelines.\n\n---\n\n## 📄 License\n\nApache License 2.0 - see [LICENSE](LICENSE) for details.\n\n---\n\n## 🔗 Links\n\n- **🌐 Website**: [preocr.io](https://preocr.io)\n- **GitHub**: [https://github.com/yuvaraj3855/preocr](https://github.com/yuvaraj3855/preocr)\n- **PyPI**: [https://pypi.org/project/preocr](https://pypi.org/project/preocr)\n- **Issues**: [https://github.com/yuvaraj3855/preocr/issues](https://github.com/yuvaraj3855/preocr/issues)\n\n---\n\n\u003cdiv align=\"center\"\u003e\n\n**Made with ❤️ for efficient document processing**\n\n[🌐 Website](https://preocr.io) | [⭐ Star on GitHub](https://github.com/yuvaraj3855/preocr) | [📖 Documentation](https://github.com/yuvaraj3855/preocr#readme) | [🐛 Report Issue](https://github.com/yuvaraj3855/preocr/issues)\n\n\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyuvaraj3855%2Fpreocr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyuvaraj3855%2Fpreocr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyuvaraj3855%2Fpreocr/lists"}