PDF Processing Utility Functions

This Python module provides various utility functions for PDF processing, including conversion, tagging, OCR, and more (This is only for educational purposes and the code here is for viewing only, please do not use for commercial distribution). From converting PDFs to images to extracting OCR data, these functions aim to streamline various tasks involved in PDF manipulation and analysis.

Setting Up Virtual Environments with pip and conda

Virtual environments are a useful tool for managing dependencies and isolating projects in Python development. They allow you to install packages and dependencies separately for each project, preventing conflicts between different projects.

This README provides instructions for setting up virtual environments using two popular Python package managers: pip and conda.

Using pip

1. Install Python: Ensure Python is installed on your system. You can download and install Python from the official website: python.org.

2. Install pip (if not already installed): pip usually comes pre-installed with Python. You can check if pip is installed by running:

   pip --version

   If pip is not installed, you can install it using the following command:

   python -m ensurepip

Creating a Virtual Environment

1. Install virtualenv (if not already installed): You can install virtualenv using pip:

   pip install virtualenv

2. Create a virtual environment: Navigate to your project directory and run the following command to create a virtual environment named venv:

   virtualenv venv

Activating the Virtual Environment

• On Windows:

  venv\Scripts\activate

• On Unix or MacOS:

  source venv/bin/activate

Deactivating the Virtual Environment

deactivate

Using conda

1. Install Anaconda or Miniconda: Anaconda and Miniconda are Python distribution platforms that come with conda, a package, dependency, and environment manager. You can download and install Anaconda or Miniconda from the official website: anaconda.com or docs.conda.io/en/latest/miniconda.html.

Creating a Virtual Environment

1. Create a virtual environment: Open a terminal or command prompt and run the following command to create a virtual environment named myenv:

   conda create --name myenv

Activating the Virtual Environment

conda activate myenv

Deactivating the Virtual Environment

conda deactivate

Dependency and Module Installation

Once you have set up your virtual environment using either pip or conda, you can install the required dependencies for your project.

Using pip

If you have a requirements.txt file listing all the dependencies, you can install them using the following command:

pip install -r requirements.txt

This command will parse the requirements.txt file and install each listed dependency into your virtual environment.

Using conda

Similarly, if you are using conda, you can install dependencies from a requirements.txt file using the following command:

conda install --file requirements.txt

This command will read the requirements.txt file and install the specified packages into your conda environment.

Function Details

1. convert_pdf_to_images(pdf_file_path: str, output_save_dir: str = '') -> list

   • This function converts a PDF file to a list of images.
   • It utilizes the pdf2image library to perform the conversion.
   • Each page of the PDF is converted to a separate image file.
   • The images are stored in the specified output_save_dir.
   • Returns a list of paths to the generated image files.

2. pdf_to_html_pdf2txt(pdf_file_path: str, output_file_path: str, redact_anchor_tags: bool = True, visualize_bboxes: bool = True) -> None

   • Converts a PDF file to an HTML file using the pdf2txt tool.
   • The resulting HTML file can be redacted to remove anchor tags and visualized to show bounding boxes.
   • Redacting anchor tags is useful for removing hyperlinks from the text.
   • Visualizing bounding boxes aids in understanding the layout of text in the PDF.
   • Saves the HTML output to the specified output_file_path.

3. get_image_size(image_path : str) -> tuple

   • Retrieves the dimensions (width and height) of an image.
   • Utilizes the Pillow library to open and process the image file.
   • Returns a tuple containing the width and height of the image.

4. add_styles_to_hocr(hocr_content : str) -> str

   • Adds CSS styles to the hOCR (HTML-based OCR) content.
   • Useful for visualizing bounding boxes around recognized text elements.
   • Uses BeautifulSoup to parse and manipulate the hOCR content.
   • Returns the modified hOCR content with added CSS styles.

5. pdf_to_hocr_html_tesseract(pdf_file_path: list, images_dir_path: str, output_hocr_file_dir: str) -> None

   • Performs OCR on images extracted from a PDF using Tesseract.
   • Converts PDF pages to images, performs OCR, and saves the OCR results in hOCR format.
   • Each image's OCR output is stored as an HTML file in the specified directory.
   • Requires the pdf2image and tesseract libraries for PDF to image conversion and OCR, respectively.
   • Removes temporary image files after processing.

6. pdf_to_hocr_images_tesseract(pdf_file_path: str, images_dir_path: str, output_images_dir: str, bbox_attribute: str = 'ocrx_word') -> None

   • Converts PDF pages to images, performs OCR using Tesseract, and plots bounding boxes on the images.
   • Useful for visualizing OCR output with bounding boxes.
   • Supports different bounding box attributes such as word, line, and paragraph.
   • Requires pytesseract for OCR and PIL for image manipulation.

7. convert_docx_to_pdf(docx_path: str, pdf_path: str) -> str

   • Converts a DOCX file to a PDF file.
   • Utilizes the FPDF library to create a PDF and python-docx to extract text from the DOCX file.
   • Returns the path to the generated PDF file.

8. convert_doc_to_pdf(doc_path: str, pdf_path: str) -> str

   • Converts a DOC file to a PDF file.
   • Uses the reportlab library to generate a PDF with custom content.
   • Returns the path to the generated PDF file.

9. convert_pptx_to_pdf(pptx_path, pdf_path) -> str

   • Converts a PPTX file to a PDF file while maintaining slide layouts and content.
   • Adds a watermark to the converted PDF.
   • Relies on the spire.presentation library for conversion.
   • Returns the path to the generated PDF file.

10. split_pdf_pages(pdf_file_path: str, output_dir: str) -> list

    • Splits a PDF file into individual pages.
    • Saves each page as a separate PDF file in the specified output directory.
    • Uses PyPDF2 library for PDF manipulation.
    • Returns a list of paths to the split PDF files.

11. extract_ocr_data_from_pdf(pdf_file_path: str, temp_image_dir: str, output_json_path: str = '', target: str = 'par', verbose: bool = True) -> list

    • Extracts OCR data (words, bounding boxes, line IDs, paragraph IDs) from a PDF using Tesseract OCR.
    • OCR results are stored as a JSON file if output_json_path is provided.
    • Supports extraction at word, line, or paragraph level (specified by target).
    • Requires pytesseract and pdf2image libraries for OCR and PDF to image conversion, respectively.

12. tag_pdf(pdf_file_path: str, output_pdf_path: str, title: str = 'Document Title', lang: str = 'en-US') -> str

    • Makes a PDF file accessible by adding tags to enhance accessibility.
    • Uses Pdfix library for PDF manipulation.
    • Tags improve the reading order and navigation of the document for users with disabilities.
    • Returns the path to the tagged PDF file.

13. extract_and_save_pdf_tags(pdf_file_path: str, output_html_path: str) -> None

    • Extracts tags from a PDF file and saves them as an HTML file.
    • Useful for visualizing the document structure and tags.
    • Requires Pdfix library for PDF manipulation.

14. parse_text(text: str) -> str

    • Handles Unicode and other escape sequence issues in the input text.
    • Replaces non-printable characters and escapes sequences with their corresponding Unicode representations.
    • Returns the processed text.

15. image_to_pdf(image_path: str, pdf_path: str) -> None

    • Converts an image file to a PDF file.
    • Uses the reportlab library to generate a PDF with the image.
    • Supports various image formats (JPEG, PNG, etc.).
    • Saves the generated PDF to the specified path.

16. extract_ocr_data_with_tags_from_pdf(pdf_file_path: str, output_json_path: str = '', target: str = 'par', verbose: bool = True) -> list

    • Extracts OCR data from a PDF and augments it with HTML tag and style information.
    • Supports extraction at word, line, or paragraph level (specified by target).
    • Generates a JSON file containing the augmented OCR data if output_json_path is provided.
    • Requires pdf2image and pytesseract libraries for PDF to image conversion and OCR, respectively.

Sample Usage

# Sample code for extracting OCR data with tags from a PDF
pdf_file_path = "pdf/example.pdf"
extract_ocr_data_with_tags_from_pdf(pdf_file_path, output_json_path="output/ocr_data.json", target='par', verbose=True)

Citation

If you find this library useful for your work, please consider citing it as follows:

[Gautam Chettiar]. PDF Processing Utility Functions. 2024, https://github.com/chettiargautam/PDF-Utilities