Find the bounds of a text string in a PDF using Python

PDF documents are a ubiquitous format for sharing information, but when it comes to programmatically interacting with the content inside these files, things can get a bit tricky. Whether you're extracting specific data or simply navigating through the text, Python offers a powerful library called pyMuPDF that can make your life much easier. In this blog post, we'll delve into the search_for method of pyMuPDF to find the text bounds of a given text within a PDF document.

What is pyMuPDF?

PyMuPDF is a Python library that provides a convenient interface for working with PDF files. It allows you to extract text, images, and metadata from PDFs, as well as manipulate and annotate these documents. PyMuPDF is built on top of the MuPDF library, which is renowned for its speed and accuracy in rendering PDFs.

One particularly useful feature of pyMuPDF is its ability to search for text within a PDF document and determine the bounding box of the text, which can be helpful for various tasks such as text extraction, text highlighting, or creating custom search functionality within your PDF viewer application.

Getting Started

Before we dive into using the search_for method, make sure you have pyMuPDF installed. You can install it using pip:

1pip install PyMuPDF

Once you have pyMuPDF installed, you're ready to get started.

Using the search_for Method

The search_for method in pyMuPDF allows you to search for text within a PDF document and obtain its bounding box coordinates. Here's how you can use it:

 1import fitz
 2
 3# Open the PDF file
 4pdf_document = fitz.open('example.pdf')
 5
 6# Specify the text you want to search for
 7search_text = 'example text'
 8
 9# Iterate through each page in the PDF
10for page in doc:
11
12    # Use the `search_for` method to find instances of the search text on the page
13    text_instances = page.search_for(search_text)
14
15    # Iterate through each instance and print the bounding box coordinates
16    for text_instance in text_instances:
17        x0, y0, x1, y1 = text_instance.bbox
18        print(f"Page {page_num + 1}:")
19        print(f"Text: {search_text}")
20        print(f"Bounding Box: ({x0}, {y0}) - ({x1}, {y1})")

In this example, we first open the PDF document using fitz.open(). Then, we specify the text we want to search for using the search_text variable. We iterate through each page in the PDF and use the search_for method to find instances of the search text on each page. For each instance, we retrieve and print the bounding box coordinates using the bbox attribute.

This works nicely for text that is spread over different lines as well. In that case, you'll get multiple bounds, one for each line of text. The only time it didn't work for me was when the text I was searching for was a date, e.g. 2023-09-13 and that was split over two lines like this:

1The date in september we are talking about 2023-09-
213, which is a Wednesday.

In that case, it didn't know that the trailing dash on the first line was a part of the date, but it thought that it was the hyphen. So, if you would search for 2023-09-13, it would not find anything. If you would search for 2023-0913, it would find the text boxes.

Practical Applications

Now that you know how to use the search_for method in pyMuPDF to find text bounds in a PDF, you can leverage this knowledge for various tasks:

1. Text Extraction: You can extract specific text elements from a PDF by searching for them and then cropping the page using the bounding box coordinates.

2. Text Highlighting: If you're building a PDF viewer or annotator, you can use the bounding box coordinates to highlight or underline the text.

3. Custom Search Functionality: Implement custom search functionality within your PDF viewer or application, allowing users to find specific text quickly.

4. Data Extraction: When dealing with structured data in PDFs, you can locate and extract data points precisely using the text bounds.

In conclusion, pyMuPDF's search_for method provides a powerful way to interact with the text in PDF documents. Whether you're building a PDF manipulation tool, a custom PDF viewer, or a data extraction script, this method can be a valuable addition to your toolkit.