PDF Document Layout Analysis is an exciting field that focuses on separating and classifying various elements within a PDF document, such as text, tables, and images. In this article, we’ll guide you through performing layout analysis with the help of a powerful service and provide troubleshooting tips to ensure smooth sailing. Let’s dive in!

Understanding PDF Layout Analysis Through Analogy

Imagine that a PDF document is like a large pizza, with various toppings scattered across it. Just like how each topping has a specific location on the pizza (pepperoni in one spot, mushrooms in another), each element within a PDF (text, images, tables) occupies a designated space on the page. The layout analysis acts like a pizza cutter, dividing the pizza into slices and identifying which toppings are on each slice. Our service helps you make sense of these elements, ensuring you know what’s where on your ‘pizza’!

Quick Start

To get started with the PDF Document Layout Analysis service, follow these steps:

• Clone the service by running the following command in your terminal:

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

• Navigate into the cloned directory:

cd pdf-document-layout-analysis

• Start the service:

make start

• Get the segments of a PDF:
• For visual models:

curl -X POST -F 'file=@/PATH/TO/PDF/pdf_name.pdf' localhost:5060

• For non-visual models:

curl -X POST -F 'file=@/PATH/TO/PDF/pdf_name.pdf' -F "fast=true" localhost:5060

• To stop the server, simply run:

make stop

Dependencies

Before diving deeper, make sure you have the following dependencies installed:

• Docker Desktop 4.25.0
• For GPU support, follow the installation guide here.

Requirements

Your environment should meet the following requirements:

• 4 GB RAM
• 6 GB GPU memory (if GPU is not available, it will run on the CPU)

Models in Action

There are two types of models you can work with:

• Visual Model (VGT): Trained by Alibaba Research Group, this model understands the full page context, providing superior performance.
• LightGBM Models: These are faster and more resource-friendly, using XML information extracted by Poppler. They may not perform as well as the visual model but are beneficial for quick analyses.

Data Overview

Our service utilizes the DocLayNet dataset for training, which includes 11 categories such as:

1. Caption
2. Footnote
3. Formula
4. List item
5. Page footer
6. Page header
7. Picture
8. Section header
9. Table
10. Text
11. Title

Usage

Using the service for segment extraction is straightforward:

curl -X POST -F 'file=@/PATH/TO/PDF/pdf_name.pdf' localhost:5060

For using the LightGBM models:

curl -X POST -F 'file=@/PATH/TO/PDF/pdf_name.pdf' -F "fast=true" localhost:5060

Interpreting the Output

The response includes a list of SegmentBox elements with details such as:

• Left position of the segment
• Top position of the segment
• Width and Height of the segment
• Page number
• Text inside the segment
• Type of segment

Benchmark Results

The benchmark results for the VGT model on the PubLayNet dataset are as follows:

Overall	Text	Title	List	Table	Figure
0.962	0.950	0.939	0.968	0.981	0.971

Troubleshooting

If you encounter issues during your PDF analysis, consider the following:

• Ensure that Docker is running and properly installed.
• Verify that your PDF file path is correct.
• Check if you have sufficient RAM and GPU resources available.
• If you continue to experience problems, feel free to visit the community for more insights or support.

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

Wrap Up

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.