PDF Intelligence Extractor

PDF Intelligence Extractor

📄 An intelligent document parser that extracts structured data from unstructured PDFs like resumes, invoices, reports, and research papers — with support for tables, totals, metadata, and key sections.

🚀 Project Overview

PDF Intelligence Extractor is a Python-based tool designed to analyze and extract key information from a wide variety of PDF files using a hybrid of rule-based (regex) and AI-assisted techniques. It supports deep parsing of layouts, tables, and sections, making it ideal for automating workflows involving:

• Resume parsing
• Invoice analytics
• Research paper summarization
• Report metadata extraction

The tool provides clean structured outputs in JSON or CSV formats, ready for downstream automation or database ingestion.

🧠 Key Features

🗂️ PDF Layout Awareness

• Parse text, headers, footers, and sections intelligently
• Detect and extract tabular data
• Read multi-column layouts

📊 Smart Table Extractor

• Uses pdfplumber or PyMuPDF for accurate table boundary detection
• Handles rotated pages, multi-page tables

🤖 AI + Regex Hybrid Extraction

• Regex patterns for known formats (e.g., invoices, phone numbers)
• Optional AI (transformer-based) to label sections semantically (e.g., "Education", "Experience", "Summary")

💾 Output Formats

• JSON for structured key-value mappings
• CSV for table data
• Plain text or Markdown for summaries

📌 Pluggable Parsers

• Easily extend support for new formats (academic papers, receipts, contracts)

🛠️ Tech Stack

Purpose	Library
PDF parsing	pdfplumber, PyMuPDF
AI/NLP extraction	transformers, spacy
Regex processing	re, regex, dateutil
Table handling	pandas
Exporting	json, csv, markdown

📂 Project Structure

pdf_intelligence_extractor/
├── extractors/
│   ├── __init__.py
│   ├── base_parser.py
│   ├── invoice_parser.py
│   ├── resume_parser.py
│   └── research_parser.py
├── utils/
│   ├── __init__.py
│   ├── table_utils.py
│   ├── file_utils.py
│   └── ai_helpers.py
├── cli/
│   ├── __init__.py
│   └── main.py
├── outputs/
│   └── sample_output.json
├── examples/
│   ├── resume.pdf
│   └── invoice.pdf
├── tests/
│   ├── __init__.py
│   ├── test_resume_parser.py
│   ├── test_invoice_parser.py
│   └── test_research_parser.py
├── README.md
└── requirements.txt

📈 Use Cases

📄 Resume Parsing

Extract candidate name, email, phone, education, skills

💰 Invoice Automation

Extract totals, dates, sender/recipient, tax fields, line items

🧪 Research Paper Analysis

Extract authors, affiliations, abstract, references

📊 Business Report Metadata

Auto-label key sections, detect anomalies in totals

📦 Installation

git clone https://github.com/yourname/pdf-intelligence-extractor.git
cd pdf-intelligence-extractor
pip install -r requirements.txt

🧪 Example Usage

# Parse a resume PDF and export to JSON
python cli/main.py --file resume.pdf --type resume --out parsed_resume.json

# Parse all invoices in a folder
python cli/main.py --dir ./invoices/ --type invoice --out ./outputs/

# Auto-detect document type and extract
python cli/main.py --file unknown.pdf --detect-type --out out.json

📤 Sample Output

{
  "document_type": "resume",
  "name": "John Doe",
  "email": "john.doe@email.com",
  "skills": ["Python", "NLP", "Data Analysis"],
  "education": [
    {
      "degree": "B.Tech in CSE",
      "institute": "XYZ University",
      "year": "2021"
    }
  ],
  "experience": [
    {
      "company": "TechCorp",
      "role": "Data Scientist",
      "duration": "Jan 2022 - Present"
    }
  ]
}

🧪 Testing

Run the test suite:

pytest tests/ -v --cov=pdf_intelligence_extractor

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Submit a pull request

📄 License

MIT License - see LICENSE file for details.