beginner's guide to extracting data from pdf documents

You’re here because you have about a hundred (or even a thousand?) documents—waiting in a folder—to be…done something with. I can relate.

Not even a month ago, our team was in a very similar situation—2,401 PDF documents: multiple pages, different types of docs…all unstructured data.

In about a short week, we tried almost every probable solution, realized why it wouldn’t work for us & ended up building something incredible of our own.

Here’s a timeline of how Document AI by Playmaker came to life.

I could just copy-paste it

The very first thought—required bare minimum effort to come up with, but would’ve cost us months of work had we considered something like this.

If it was a few documents? Sure, I would’ve run with this solution. Unfortunately for us, if, on average, a 5-page document took about 10 minutes to process, 2.4K documents would take ~16 days non-stop. Not very…ideal.

Chat…GPT?

It’s like the AI Hail Mary solution…if nothing else works, it…should work…right?

No. AI tools (not limiting to just ChatGPT) are unreliable, along with a few other issues like…

• Data validation is challenging without manual checks, defeating the purpose of trying to automate it with AI.
• AI can extract data but can’t structure it, leaving us with the task of organizing it. Plus, we still need to copy-paste it to our destination (worst-case scenario).
• AI will hallucinate, potentially introducing errors into your dataset.

Python Libraries

Python libraries like PyPDF2, PDFMiner & Textract could work out great, but they come with nuances of their own…

• You need to write custom rules to validate data.
• You can only consume specific formats of documents.

The big bummer? You need to be highly technical, and I know most people working with documents aren't.

Document Management Systems (DMS)

If you're dealing with a lot of documents, a DMS could help you to...

• Index and organize your documents.
• Make them searchable.
• Manage version control.
• Manage sharing and access control.

There are quite a few DMS out there, like DocuWare and Laserfiche at the enterprise price-point and Teddy or Paperless-NGX, which are open-source.But? You know there's a but...

• It's insanely expensive. Most document management systems are built for the enterprise, and per-seat costs range from $100 to $275.
• Lock-in contracts. It doesn't need an explanation—with enterprise products; you generally need to sign 6-12 months of contracts with minimum billing clauses.
• Doesn't integrate with your workflows. Credit where due—major DMS like DocuWare announced workflow integrations recently, but they still lag behind when it comes to integrating with modern tools & platforms.

Optical Character Recognition

OCR can be game-changing for scanned documents or images containing text.

It converts visual text into machine-readable format & you could run it through something like ChatGPT or Claude.

But…(yes, there’s another but) it fails big time when dealing with anything handwritten or low-quality scans.

The best practices

In the short week of trying anything and everything, we failed about a few dozen times—and took notes of what we shouldn’t do—and flipped it to make it into best practices for document processing:

1. Standardize Input Documents: When possible, encourage the use of standardized templates for documents like invoices or forms. This can significantly simplify the extraction process.
2. Preprocess Documents: Clean up and optimize PDFs before extraction. This might involve removing unnecessary elements or converting scanned documents to searchable PDFs.
3. Use Multiple Techniques: To achieve the best results, employ a combination of extraction methods for different types of documents or data.
4. Implement Quality Control: Set up validation rules and human review processes to ensure the accuracy of extracted data.
5. Continuously Train and Improve: If using AI-powered solutions, regularly update and refine the models with new examples to improve accuracy over time.
6. Consider Data Privacy and Security: Ensure that your chosen extraction method complies with relevant data protection regulations and security standards.

Playmaker Document AI

After exploring various solutions, we created Playmaker Document AI to address our specific document processing needs. Here's what makes it stand out:

• Versatility: Handles multiple document formats (PDFs, images, text files)
• AI-Powered Extraction: Using advanced AI, it adapts to each unique document structure for accurate data extraction.
• Built-in Validation:Custom rules ensure the extracted data meets your specific criteria.
• Seamless Integration: Connects with 300+ tools, fitting right into your existing workflows

This isn't just a standalone solution—it's a crucial part of our broader strategy for Playmaker as a workflow automation product. By addressing the document processing bottleneck, we're enabling businesses to create end-to-end automated workflows. From document intake to data extraction and integration with other business processes, Playmaker aims to eliminate manual work across organizations.By combining Document AI with our other automation capabilities, we're building a comprehensive platform that can tackle complex, multi-step workflows involving document processing, data management, and cross-tool interactions. It's our way of unlocking the full potential of documents and seamlessly integrating that information into broader business workflows.

Real-world Applications

PDF data extraction has numerous practical applications across various industries:

1. Finance: Automating data extraction from invoices, receipts, and financial reports for faster processing and analysis.
2. Healthcare: Extracting patient information from medical records or research data from academic papers for more efficient record-keeping and analysis.
3. Legal: Quickly extracting critical information from contracts and legal documents, saving hours of manual review.
4. Human Resources: Automating data extraction from resumes and job applications to streamline recruitment.
5. E-commerce: Converting product catalogues from PDFs into structured data for online stores, ensuring accurate and up-to-date product information.

Conclusion

Extracting data from PDFs doesn't have to be a Herculean task. While there are various solutions available, each with its pros and cons, tools like Playmaker Document AI offer a balanced approach that combines ease of use, accuracy, and flexibility.

The key is to find a solution that not only extracts data efficiently but also integrates seamlessly with your workflow, allowing you to unlock the full potential of your document-based information.

By leveraging the right tools and following best practices, you can transform your PDF data extraction process from a time-consuming chore into a streamlined, value-added operation. Your future self (and your productivity metrics) will thank you.