Have you ever struggled to extract structured data from multi-column PDFs, like technical articles or reports? I know I have. The task can be daunting, especially when dealing with complex layouts and varying content formats.
Recently, I embarked on a project to ingest multi-column PDFs and extract a structured model, including headers, sections, tables, and more. I set up a pipeline on Windows in Python 3.11 using Detectron2 (PubLayNet-faster_rcnn_R_50_FPN_3x) via LayoutParser for layout segmentation and Tesseract OCR for text.
While the results were mediocre, I realized that the structure wasn’t being detected correctly, and the processing was quite slow on long documents. That’s when I turned to the Reddit community for help.
## The Goal: Retrieving a Structured JSON
The ultimate goal is to retrieve a structured JSON from these documents, where the content is stored in a hierarchical format. For example:
{
"title": "...",
"sections": [
{
"heading": "Introduction",
"level": 1,
"content": "",
"subsections": [
{
"heading": "About Allianz",
"level": 2,
"content": "Allianz Australia Insurance Limited ..."
}
]
}
]
}## Tips for Successful Data Extraction
After researching and experimenting, I’ve gathered some valuable tips to share with you:
– **Choose the right tools**: Detectron2 and LayoutParser are powerful tools for layout segmentation, but you may need to experiment with different models and configurations to achieve the best results.
– **Pre-process your PDFs**: Clean and normalize your PDFs before feeding them into your pipeline. This can significantly improve the accuracy of your layout segmentation and OCR.
– **Optimize your pipeline**: Experiment with different processing steps and optimize your pipeline for performance. You may need to trade off accuracy for speed or vice versa.
– **Post-process your data**: Don’t rely solely on your pipeline to produce perfect results. Implement post-processing steps to correct errors and refine your extracted data.
## Conclusion
Extracting structured data from multi-column PDFs is a challenging task, but with the right tools and techniques, you can achieve remarkable results. By following these tips and experimenting with different approaches, you’ll be well on your way to unlocking the data hidden within these complex documents.
—
*Further reading: [PDF Layout Analysis with Detectron2 and LayoutParser](https://towardsdatascience.com/pdf-layout-analysis-with-detectron2-and-layoutparser-94f7c1f6f3a)*