PDF files are widely used for storing structured information, but extracting readable text from them can be challenging without the right tools. Python developers often need to automate document parsing for tasks like compliance, healthcare records, or search indexing. Viewer library offers a powerful way to extract text from PDF using Python with full access to lines, words, and characters. This guide explains how to use the Viewer API to retrieve structured text from PDF files. Whether you’re building a backend service or a desktop utility, this approach helps you unlock the content inside PDFs with minimal effort and high accuracy. Following steps explain how to extract text from PDF in Python.

Steps to Extract Text from PDF using Python

1. Install GroupDocs.Viewer for Python via .NET using pip
2. Import groupdocs.viewer and groupdocs.viewer.options modules
3. Create a Viewer instance by passing the path to your PDF file
4. Use ViewInfoOptions.for_html_view() to prepare view settings
5. Enable text extraction by setting extract_text = True
6. Call viewer.get_view_info() to retrieve structured page data
7. Loop through each page and access its lines, words, and characters
8. Print or process the extracted text as needed

To perform Python extract data from PDF, you first install GroupDocs.Viewer and import the required modules. Then, you instantiate the Viewer class with your PDF file path and configure the view options using ViewInfoOptions.for_html_view(). By setting extract_text = True, you enable detailed text extraction. The get_view_info() method returns page-level data, including lines, words, and characters. You can loop through each page and print or process the extracted content. This method supports UTF-8 encoding, making it ideal for multilingual documents. The code is efficient, clean, and suitable for production-grade applications.

Code to Extract Text from PDF using Python

In summary, extracting text from PDF using Python is a practical and efficient way to unlock valuable content from static documents. With GroupDocs.Viewer, developers can access structured data including lines, words, and characters—ideal for building search engines, audit systems, or data pipelines. The process is clean, scalable, and supports multilingual output through UTF-8 encoding. Whether you’re working in healthcare, legal tech, or enterprise automation, this technique empowers you to transform PDFs into actionable data. By integrating text extraction into your Python workflows, you gain precision, control, and flexibility across platforms. It’s a vital skill for modern document-driven applications.

To learn more about this powerful feature, we recommend reading our comprehensive tutorial on how to render PDF as HTML using Python and unlock new possibilities for your document workflows