# Simplifying PDF Parsing: Extracting Headings and Subheadings by Converting PDF to HTML

Parsing PDF files can be a complex task, especially when you need to extract specific information like headings and subheadings. Most libraries designed to handle PDFs are excellent at reading the file format but can be tricky when you’re trying to extract structured content such as document outlines or nested categories. For this reason, I sought to simplify this process by converting the PDF into HTML, a format that’s much easier to manipulate, and then parsing the information using Python.

In this post, I’ll walk you through how I approached the problem and how you can use a simplified method to achieve the same results — extracting and organizing headings and subheadings from PDF files into a structured CSV format.

Why Convert PDF to HTML? PDFs are notoriously difficult to parse. While they present content visually, they don’t provide inherent structure like an HTML document. This can make extracting specific content, such as headings or subheadings, particularly challenging.

HTML, on the other hand, is structured and easier to work with. By converting a PDF into HTML, I could take advantage of the web-like format to access elements through their tags and styles, making it easier to classify different parts of the document.

The Problem: Extracting Document Structure from a PDF Many documents, such as research papers or reports, come with multiple layers of headings and subheadings. Identifying and extracting this hierarchy in a structured way (like in a CSV file) can be useful for content indexing, summarizing, or building knowledge graphs. However, traditional PDF parsing methods tend to focus on raw text extraction, which doesn’t retain this structure.

The Solution: Simplified Approach with Python I aimed to streamline the process by converting the PDF into HTML, which preserves the visual layout, including font sizes, boldness, and indentation. These attributes can be mapped to headings and subheadings, making it easy to distinguish between different levels of content.

Here’s a step-by-step breakdown of how I approached the solution:

Step 1: Convert PDF to HTML The first step was to convert the PDF file into HTML. For this, I used pdfminer, a Python library that provides excellent functionality for processing PDFs. With pdfminer, I extracted the content of the PDF and wrote it into an HTML file.

Step 2: Parse HTML and Extract Headings/Subheadings Once the PDF content is converted to HTML, the next challenge is to parse the HTML and classify content as headings or subheadings. For this, I used BeautifulSoup, a powerful Python library for parsing HTML.

Step 3: Putting It All Together With the PDF converted to HTML and the HTML parsed to extract structured content, the final step was to run the parser as part of an end-to-end function.

def parser(): pdf_file_path = get_pdf_file() processed_html = pdftohtml(pdf_file_path) html_to_csv(processed_html)

parser() This approach is both simple and powerful, making it easy to handle a wide variety of PDF formats. Whether you’re building an indexing tool, summarizing long documents, or preparing structured data for further analysis, this solution can help streamline your workflow.

Next Steps I plan to further enhance this project by adding the ability to detect other document elements, such as tables or lists. If you’re working with complex documents, this process can be easily extended to cover more nuanced content extraction needs.

If you’re interested in trying this approach, check out the full code on my github and let me know what you think!

Thank you for reading until the end. Before you go: