PDF Inventory Parser (Jupyter Notebook)

A Jupyter notebook-based tool for extracting structured inventory data from PDF documents.

Project Overview

This project uses Jupyter notebooks to parse PDF files containing inventory information and convert them into structured JSON data. It extracts:

• Owner information (name, address, telephone)
• Inventory items with their descriptions, purchase dates, serial numbers, and values

Requirements

• Python 3.8+
• Jupyter Notebook/Lab
• Required libraries:
  • PyPDF2 (or similar PDF extraction library)
  • datetime
  • re
  • json

Installation

git clone https://github.com/yourusername/pdf-inventory-parser.git
cd pdf-inventory-parser
pip install -r requirements.txt
jupyter notebook

Usage

1. Open inventory_parser.ipynb in Jupyter
2. Update the pdf_path variable with the path to your PDF file
3. Run all cells in sequence
4. The structured data will be saved as extracted_inventory.json

Project Files

• inventory_parser.ipynb - Main notebook containing all extraction and parsing code
• requirements.txt - Required Python dependencies
• sample/ - Sample PDF files for testing
• output/ - Directory where JSON output is saved

Notebook Structure

The notebook is divided into these main sections:

1. Setup - Imports and initialization
2. Data Models - Classes for Owner and Inventory data
3. PDF Processing - Functions to extract and clean text from PDFs
4. Date Parsing - Functions to handle different date formats
5. Data Extraction - Functions to identify and extract structured data
6. Run Pipeline - End-to-end process execution
7. Output Results - Data visualization and JSON export

Output Format

{
  "owner_name": "Owner Name",
  "owner_address": "Owner Address",
  "owner_telephone": "Owner Phone Number",
  "data": [
    {
      "purchase_date": "2025-04-25T20:50:51",
      "serial_number": "12345",
      "description": "Item Description",
      "source_style_area": "Source Style Area",
      "value": "500"
    }
  ]
}

License

MIT