In this article, we’ll explore how to segment and classify different parts of PDF pages using a powerful service called PDF Document Layout Analysis. This model is capable of identifying various elements such as texts, titles, images, and tables, while also determining their correct order. Let’s get started!

Quick Start

To begin using the PDF Document Layout Analysis service, you need to set it up in your environment. Here’s how to clone and start the service:

• Clone the service:

  git clone https://github.com/huridocs/pdf-document-layout-analysis.git

• Navigate into the directory:

  cd pdf-document-layout-analysis

• Start the service:

  make start

Extracting Segments from a PDF

Now that the service is running, you can start extracting segments from a PDF file. Use the following commands:

• For visual models:

  curl -X POST -F file=@PATHTOPDF/pdf_name.pdf localhost:5060

• For non-visual models:

  curl -X POST -F file=@PATHTOPDF/pdf_name.pdf -F fast=true localhost:5060

Understanding the Code: A Simple Analogy

Imagine you are a librarian who needs to organize a chaotic library of books (the PDF). The librarian (your service) has two methods to sort books:

• The first method (visual model) allows the librarian to see the covers and read the titles, making it easy to determine where each book belongs, much like how the visual model utilizes the entire page to analyze content.
• The second method (non-visual model) relies purely on the catalog and the metadata (just the XML data extracted) which lets the librarian know what books they have but doesn’t give them the full picture of where they should go. While it’s quicker, it may not be as accurate as physically viewing the books.

The librarian can produce a well-organized library (output of segments) using the information from both methods to create a better-ordered and categorized library space.

Troubleshooting Ideas

If you run into issues, here are some troubleshooting tips you can use:

• Ensure Docker Desktop is properly installed. You can download it here.
• If you want to utilize GPU support, refer to the installation guide.
• Check your RAM and GPU memory requirements. The service needs at least 4 GB of RAM and 6 GB of GPU memory to operate efficiently.
• Make sure you have correctly referenced the path to the PDF file you wish to analyze.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.