🏠 Floorplan Dimension Extractor

A sophisticated Python pipeline for automatically extracting dimensions and cabinet codes from architectural floorplan PDFs. This tool converts various dimension formats into standardized measurements and provides structured output with visualization capabilities.

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🚀 Features

• PDF Text Extraction: Support for both PyMuPDF and pdfplumber libraries
• Smart Dimension Parsing: Handles multiple formats (inches, feet-inches, fractions)
• Cabinet Code Detection: Identifies appliance and cabinet codes
• Visualization: Draws bounding boxes around detected elements
• Web Interface: User-friendly Streamlit dashboard
• Structured Output: JSON format with spatial coordinates
• Batch Processing: Command-line interface for automation

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🛠 Tech Stack

Core Technologies

• Python 3.8+ - Primary programming language
• PyMuPDF/fitz - High-performance PDF text extraction
• pdfplumber - Alternative PDF processing with detailed layout analysis
• Streamlit - Web application framework
• OpenCV - Image processing and visualization
• Regex - Advanced pattern matching

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Supporting Libraries

• Pillow - Image manipulation
• NumPy - Numerical operations
• Matplotlib - Data visualization (for internal analysis)

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📁 Project Structure

floorplan-dimractor/
├── data/
│ ├── input/ # Uploaded PDF files
│ └── output/ # JSON extraction results
├── src/
│ ├── init.py
│ ├── pdf_processor.py # PDF text extraction
│ ├── dimension_parser.py # Dimension parsing logic
│ ├── code_detector.py # Cabinet code detection
│ ├── visualizer.py # Bounding box visualization
│ └── utils.py # Utility functions
├── tests/
│ └── test_extractor.py # Unit tests
├── main.py # Command-line interface
├── app.py # Web interface
├── requirements.txt # Python dependencies
└── README.md

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📦 Installation

1. Clone the Repository

   git clone https://github.com/jasoncobra3/Floorplan-Dimractor.git
   cd Floorplan-Dimractor
   
   

2. Create Virtual Environment

    python -m venv venv
   

3. Activate the Virtual Environment

    # Windows:
    venv\Scripts\activate
    # macOS/Linux:
    venv/bin/activate
   

4. Install Dependencies

   pip install -r requirements.txt
   
   

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🚀Run the App

Run the Script in Terminal

  streamlit run app.py

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Advanced Dimension Parsing

• Regex Patterns: Comprehensive pattern matching for various formats
• Fraction Handling: Converts mixed numbers and fractions to decimals
• Unit Conversion: Automatic feet-to-inches conversion
• Spatial Analysis: Bounding box coordinates for each detection

Modular Design

• Separation of Concerns: Each component handles specific responsibilities
• Extensible Architecture: Easy to add new parsers or detectors
• Error Handling: Graceful degradation and informative error messages

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📸 Screenshots

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🎯 Challenges & Solutions

1. Challenge 1: PDF Text Extraction Variability
   Problem: Different PDF generators create varying text layouts and encoding.

   Solution:

   • Implemented dual-library approach (PyMuPDF + pdfplumber)
   • Combined text extraction with spatial analysis
   • Added fallback mechanisms for different PDF types

2. Challenge 2: Dimension Format Diversity
   Problem: Architectural drawings use multiple dimension formats:

   • 25" (Simple inches)
   • 2' 6" (Feet and inches)
   • 34 (1/2)" (Fractions)
   • 25 3/4" (Mixed numbers)

   Solution:

   • Created comprehensive regex patterns
   • Implemented format-specific parsers
   • Added validation and error recovery

3. Challenge 3: Bounding Box Accuracy
   Problem: Text bounding boxes don't always match visual elements.

   Solution:

   • Combined multiple text extraction methods
   • Implemented coordinate normalization
   • Added visualization for manual verification

4. Challenge 4: Performance Optimization
   Problem: Large PDF files with complex layouts can be slow to process.

   Solution:

   • Implemented efficient text filtering
   • Added progress tracking
   • Provided multiple processing options

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📊 Approach Comparison: PyMuPDF vs pdfplumber

Feature	PyMuPDF	pdfplumber
Speed	Very Fast	Moderate
Text Accuracy	Good	Excellent
Layout Analysis	Basic	Excellent
Bounding Box Precision	Good	Excellent
Ease of Use	Very Easy	Easy
Memory Usage	Very Efficient	Moderate
Installation	Very Easy	Easy
Documentation	Good	Excellent
Complex Layout Handling	Limited	Excellent
Text Extraction Quality	Good	Excellent
Table Extraction	Basic	Excellent
Image Extraction	Excellent	Good
Performance with Large Files	Excellent	Good
Coordinate Accuracy	Good	Excellent
Font Information	Basic	Detailed
Active Maintenance	Yes	Yes

Summary

• PyMuPDF (fitz) excels in speed and memory efficiency, making it ideal for processing large numbers of PDFs quickly. It's straightforward to use and handles basic text extraction well.
• pdfplumber provides superior layout analysis and text extraction accuracy, especially for complex documents. It offers more detailed information about text positioning and structure but at the cost of performance.

Recommendation

• Choose PyMuPDF for: High-volume processing, simple documents, when speed is critical
• Choose pdfplumber for: Complex layouts, accurate spatial data, detailed text analysis

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If you find this project useful, please give it a ⭐!