The Best PDF Data Extraction Software [2025]

Why Is Extracting Data from PDFs So Hard?

PDFs are everywhere: invoices, contracts, insurance forms, lab results, shipping manifests and more. And yet, they remain one of the hardest formats for automation.

The Challenge: Built for Eyes, Not for Machines

The PDF (Portable Document Format) was created in the early 1990s by Adobe with a simple goal: to preserve how documents look across devices and printers. It prioritises visual fidelity, not data structure. In essence, a PDF is a digital snapshot of a page rather than a true digital document with semantic meaning.

This visual-first design creates major technical challenges for any system attempting to extract data automatically.

1. Inconsistent Formatting and Layouts

   Unlike structured data formats (like CSV or JSON), PDFs don’t follow predictable schemas. Even within a single organisation, two invoices from the same vendor might have different layouts, with the total appearing at the top right in one file and at the bottom left in the next.

   This inconsistency breaks traditional rule-based extractors, which rely on fixed templates or coordinates to find data. Each variation forces human operators to intervene, update rules, or manually correct the output.

   
   

2. Scanned vs. Digital PDFs

   Not all PDFs are created equal. A digital PDF, generated directly by software, contains a text layer, meaning its words can be selected, searched, or copied. A scanned PDF is simply an image of a printed page with no underlying text for machines to read.
   
   Ask for a document, and someone might send a scan. Ask for a digital PDF, and they’ll often embed that same image in a PDF wrapper, which is still unreadable by machines because it lacks any text layer.

   It gets even messier when users convert between formats. An employee might turn a PDF into a Word document for quick edits, then export it back to PDF, introducing broken text layers, misaligned tables, and corrupted structures.
   
   To extract information from these scanned or malformed files, modern systems rely on Optical Character Recognition (OCR), technology that converts pixel-based images into characters. OCR has advanced dramatically in recent years, now capable of handling low-quality scans, handwriting, and complex layouts.
   
   

   

   
   
   

3. Embedded Images, Watermarks, and Noise

   Real-world documents are rarely clean. Logos, background images, signatures, stamps, and watermarks are often embedded directly into PDFs, sometimes intentionally, sometimes as artefacts of a scan or export. Each of these elements introduces visual noise that can interfere with text recognition and data extraction.

   For example, a watermark layered behind text can cause an OCR engine to misread characters, mistaking a faint grey line for part of a letter. Company logos or decorative headers may be incorrectly detected as text blocks, throwing off positional alignment. Even small visual artefacts like dotted lines or form boxes can break word boundaries, causing models to fragment key fields such as policy numbers or invoice totals.

   
   

4. Complex Structures and Nested Data

   Many documents don’t store information in simple key–value pairs. Tables, nested line items, and multi-page invoices all require models to understand context, not just text.

   For example, identifying which line item corresponds to which subtotal or tax amount requires recognising spatial relationships between elements on the page. This is far beyond the capabilities of traditional OCR and template-based tools, which read text but not structure.
   
   Modern Intelligent Document Processing (IDP) systems combine OCR with machine learning and natural language processing (NLP) to infer these relationships, allowing them to handle more complex layouts. We'll explore this technology in greater detail below.

The Evolution from Manual Entry to AI Extraction

The journey to solve the PDF data problem has seen several stages. It began with pure manual data entry, a process that is not only slow but also has a typical error rate of around 1%.

Early automation tried to bridge that gap. OCR made it possible to extract text from scanned pages, turning static images into searchable documents, but it couldn't understand context or meaning. It could tell you that a number was “1234”, but not whether that number was an invoice total, a customer ID, or a date. Human operators still had to review, correct, and copy data into the right systems.

Today, we’ve reached the next stage: AI data extraction. Instead of treating every document as a unique layout problem, modern AI platforms offer the ability to understand structure, relationships, and semantics. They can identify entities like company names, dates, and totals, regardless of how they’re formatted.

Why Can't ChatGPT Read My PDF?

AI represents a major leap forward in PDF data extraction. However, widely used platforms like ChatGPT, Gemini, and Claude still have limitations. They can occasionally forget that they’re capable of performing OCR or accessing PDFs, and may even attempt to write custom scripts to convert PDFs into text-readable formats (often unsuccessfully.)

The more serious issue arises when, instead of admitting “I can’t read this PDF,” these models fabricate data and present it as if it were real.

Below is an example of this behavior. Notice how the model internally reasoned “I can’t directly read the contents of your PDF,” but this statement appeared only in its 'thinking', not in the provided answer. In other words, ChatGPT confused its internal thought process with the message it was supposed to communicate to the user. Believing it had already acknowledged the limitation, it omitted the disclaimer from its final response.

This can be both frustrating and risky, as users may not realize that the output they've been presented is entirely fabricated.

This is precisely the kind of problem V7 Go addresses effectively. Its advanced pre-processing pipeline ensures that PDFs are made AI-readable before extraction begins, eliminating hallucinated results and ensuring consistent, verifiable data quality.

AI Agents for PDF Data Extraction

Extraction is only one part of the puzzle. The real business value comes when extracted data flows directly into downstream workflows (validation, approval, reconciliation, and analysis) without human handoff.

An emerging and particularly exciting way to connect PDF data extraction with downstream automation is through AI agents.

See an example of this below in V7 Go, as an NDA PDF is automatically analyzed and key entities identified and extracted.

You can learn more about AI agents, how they are used, and how straightforward it can be to create one in V7 Go in our blog: How to Create an AI Agent Without Code: A Practical Guide.

To give an example, V7 Go can ingest a wide range of PDFs and many other file formats, from invoices and shipping manifests to contracts and reports. Once uploaded, an AI agent within V7 Go can analyze the document, extract the relevant information, and convert it into structured, machine-readable data.

From there, the agent can:

• Pull key entities (e.g., vendor names, payment amounts, PO numbers).

• Trigger next-step actions, such as routing for approval or matching to purchase orders.

• Compare extracted data against internal systems to flag discrepancies.

• Feed structured data into analytics dashboards or financial systems.

• And much more, depending on your bespoke workflow needs.

By combining intelligent extraction with interconnected AI agents, organisations can replace repetitive manual work with systems that learn, adapt, and continuously improve, finally realising the long-promised vision of a truly digital, paperless workflow.