Analyze a Scanned PDF

This project demonstrates how to extract text and structured data from a scanned PDF document using Optical Character Recognition (OCR). It uses Python with libraries like PyMuPDF, Tesseract, OpenCV, and Pillow.

Features

• Loads a scanned PDF.
• Converts PDF pages to images.
• Pre-processes images to improve OCR accuracy (grayscale, contrast, sharpness, filtering, thresholding).
• Extracts text from the images using Tesseract OCR.
• Extracts structured data (like names, dates, amounts) using regular expressions.
• Draws bounding boxes around detected words and key fields on the document image.

Requirements

• Python 3.x
• Tesseract OCR engine
• The following Python packages:
  • pymupdf
  • pytesseract
  • opencv-python
  • pillow
  • numpy
  • jupyter

How to Run

1. Clone the repository:

   git clone https://github.com/jadhavkrushna/Analyze_a_Scanned_PDF-.git
   cd Analyze_a_Scanned_PDF-

2. Install Tesseract:

   • Windows: Download and install Tesseract from the official repository. Make sure to add the installation path to your system's PATH environment variable or update the path in the notebook:

     pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'

   • macOS:

     brew install tesseract

   • Linux (Ubuntu/Debian):

     sudo apt install tesseract-ocr

3. Install Python dependencies:

   pip install -r requirements.txt

   (Note: You may need to create a requirements.txt file or install packages directly as shown in the notebook)

4. Launch Jupyter Notebook:

   jupyter notebook

5. Open and run the notebook:

   • Open the Analyze_a_Scanned_PDF_project.ipynb file in Jupyter.
   • Update the pdf_document_path variable to the location of your scanned PDF file.
   • Run the cells sequentially.

Input

A scanned PDF file. The project is configured to work with a sample mortgage document, but it can be adapted for other scanned documents. You will need to provide the path to your PDF file in the notebook.

Example:

pdf_document_path = r"c:\Users\adminpc\Downloads\sample_mortgage_document.pdf"

Output Sample

The project produces several outputs:

1. Extracted Text: A raw string of text extracted from the document.
2. Structured Data (JSON): Key information extracted from the document in JSON format.

   {
       "document_title": "Mortgage",
       "borrower_names": [
           "Ro Leading",
           "Mercia Fill",
           ...
       ],
       "lender_name": null,
       "loan_amount": null,
       "property_address": null,
       "document_dates": []
   }

3. Image with Bounding Boxes: An image of the document page with bounding boxes drawn around detected words and key fields.