https://github.com/jasoncobra3/floorplan-dimractor

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.
https://github.com/jasoncobra3/floorplan-dimractor

architecture-tools automation-tools blueprint-analysis cad-automation computer-vision dimension-extraction document-processing document-processing-pipeline floorplan-analysis image-processing measurement-tools opencv pdf-parser pdf-processing pdfplumber pymupdf streamlit text-detection

Last synced: 3 months ago
JSON representation

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.

• Host: GitHub
• URL: https://github.com/jasoncobra3/floorplan-dimractor
• Owner: jasoncobra3
• Created: 2025-09-28T10:01:43.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2025-09-28T12:40:27.000Z (3 months ago)
• Last Synced: 2025-09-28T14:36:38.656Z (3 months ago)
• Topics: architecture-tools, automation-tools, blueprint-analysis, cad-automation, computer-vision, dimension-extraction, document-processing, document-processing-pipeline, floorplan-analysis, image-processing, measurement-tools, opencv, pdf-parser, pdf-processing, pdfplumber, pymupdf, streamlit, text-detection
• Language: Python
• Homepage:
• Size: 2.16 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# 🏠 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.

![Python](https://img.shields.io/badge/Python-3.8%2B-blue)

![Streamlit](https://img.shields.io/badge/Streamlit-1.28.0-red)

![PyMuPDF](https://img.shields.io/badge/PyMuPDF-1.23.7-green)

---

## 🚀 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

---

## 🛠 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

---

### Supporting Libraries

- **Pillow** - Image manipulation

- **NumPy** - Numerical operations

- **Matplotlib** - Data visualization (for internal analysis)

---

## 📁 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

```

---

## 📦 Installation

1. **Clone the Repository**

   ```bash

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

   cd Floorplan-Dimractor

2. **Create Virtual Environment**

   ```bash

    python -m venv venv

   

3. **Activate the Virtual Environment**

   ```bash

    # Windows:

    venv\Scripts\activate

    # macOS/Linux:

    venv/bin/activate

4. **Install Dependencies**

   ```bash

   pip install -r requirements.txt

---

##  🚀Run the App

   **Run the Script in Terminal**

   ```bash

     streamlit run app.py

   ```

---

## 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

  

---

## 📸 Screenshots

|![](src/Screenshots/Screenshot_1.png)| ![](src/Screenshots/Screenshot_6.png) | ![](src/Screenshots/Screenshot_2.png) |

|----------------|------------|---------------|

| ![](src/Screenshots/Screenshot_3.png) |![](src/Screenshots/Screenshot_5.png)|![](src/Screenshots/Screenshot_4.png)|

---

## 🎯 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

  

  ---

## 📊 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

---



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