Top methods for PDF parsing: which should you use?

The portable document format — ubiquitously referred to as the PDF — has long been the gold standard for digital document sharing: customizable, universal, and compact. But the very qualities that make the filetype so useful can also make PDFs rigid and hard to interface with.

Turn messy data from PDFs and invoices into structured datasets using AI.

Think of PDFs as artisanal glass vaults: They're excellent at preserving documents according to the maker’s intention, but it can be challenging to get inside without breaking stuff. For workflows that utilize PDFs, this means a lot of valuable data stays locked inside, just out of reach.

To get a little more insight, we spoke with some members of Parabola’s Product, Engineering, and GTM teams about PDF parsing: Here are the use cases, challenges, and best practices you need to know if you work with PDFs.

But first, let’s cover the basics.

What is PDF parsing?

PDF parsing is the process of extracting text, images, and or any other data from a PDF file.

From a high level, the process of parsing includes analyzing and identifying specific elements throughout a file, and then pulling out those specific elements.

Beyond text and images, that might also include fonts, layouts, tables, and even metadata.

What is PDF parsing used for?

PDF parsing is used by professionals across many industries, most generally to pull information from one document, to then repurpose and use more specifically in another place.

In many cases, that means pulling information from a PDF to input into an Excel file to manipulate as part of a dataset, to be used for specific workflows.

Example workflows that utilize PDF parsing:

• Invoice Automation: Invoice number, date, items purchased, and payment amounts can be extracted to automate invoice processing and payments.
• Purchase Order and Receipt Processing: Refunds and reimbursements can be automated by parsing items, dollar amounts, dates, etc.
• Legal, Medical, Governmental Records Analysis: Any in-depth analysis that requires the identification and/or extraction of names, dates, citations, dollar amounts, medications, and more, all make great use of parsing.
• Financial and Insurance Processing: Similar to analysis, PDF parsing is a very commonly used by companies assessing risk and analyzing balance sheets.
• Survey/Form Analysis: Text extraction is very helpful to pull responses and collect information from forms and surveys.
• Resume Extraction: Parsing makes it simple for recruiters to filter and analyze resumes based on candidate details, contact information, work experience, and more.

Essentially any type of reporting, analysis, or archiving can utilize PDF parsing at one point or another.

The challenges that can arise from PDF parsing, however, typically surface when it’s needed to be done at scale.

Challenges of PDF Parsing

There are many benefits of using PDFs.

PDFs are secure. They’re compatible with any device. They compress files to very convenient sizes. They’re easy to scan, and ideal for printing.

There’s a reason why they’re used for so many essential business documents and processes.

The discourse around PDFs, however, has also always been about how difficult it is to extract and translate information from them.

PDFs are limiting. The same characteristics that make them great are why they’re complicated to work with when digitizing documents.

1. PDFs are a bit rigid in nature. While that is what contributes to the format’s consistency, it’s also what makes them harder to manipulate.

2. Unstructured data presents a major roadblock to being able to quickly analyze the contents of a file and extract needed information.

The main challenge for most parsing software is that, much like the paper documents they aim to mirror, PDF formats can vary widely. Though parsing tools may offer consistency, they tend to lack flexibility.

Parabola engineer Jordan Lawler notes that, in order to process a particular document type quickly and accurately, some parsers need to be trained on hundreds of instances of the same document. This requires great upfront time investment, which can be virtually undone by small changes to the source document.

Plus, even using a flawless PDF parser can be a needlessly manual process, says Adam Reisfield, Special Projects Lead at Parabola: “Today, realistically, I would just give the PDF to ChatGPT, but the limitation there is that’s not an operationalized part of my process. Next time I receive a PDF, I’m gonna have to open up ChatGPT, drop that PDF in there — it’s not actually helping me automate that whole end-to-end process.”

For all of these reasons, although PDF parsing tools are widely useful, they can be brittle and hard to apply at scale, particularly for a process like a freight audit or a complex logistics workflow, which might involve thousands of PDFs using a range of layouts and formats.

When and how to parse PDFs

Brian Sanchez, Product Manager at Parabola, emphasizes that “the best PDF parser is a person.” People are great at using context to decipher unclear data, and can quickly integrate new learnings. That said, a repetitive process like data entry can be error-prone, and securing headcount to sift through data manually comes at a cost to the business.

A competent PDF parsing software can take something that might be a highly manual process (scanning a table and copying values to another document piece by piece), and make it near-instantaneous. Here are some use cases where PDF parsing can make a difference:

Parcel invoice audit

Billing discrepancies might be small on an individual invoice, but differences in shipping costs can quickly add up. A parcel invoice audit is a meticulous process, often involving many different document types, but a PDF parser can deliver value quickly by pulling out only the most relevant data.

Inventory reconciliation

Particularly during busy shopping seasons, maintaining accurate inventory is a challenging but critical business operation. Inventory reconciliation typically involves pulling real-time figures from several sources (sometimes in messy formats) — exactly the kind of task for which a PDF parser is well-suited.

Order management

A platform like Shopify delivers order information in consistent templates, but every new channel can mean another structure to show cost of goods sold (COGS), as well as variable shipping costs. Using a PDF parser to process orders enables you to work across documents and bring your business data into a consistent, readable form.

Methods for PDF parsing

So you’re looking to run a complex freight audit involving thousands of invoices: What solutions are widely available, and what are their strengths and weaknesses?

Online converter/parser

Before wading through parsing options, one common solution is to Google “convert PDF to ______,” typically with Excel or CSV as the destination filetype.

The advantage of an online converter like Zamzar or Smallpdf, or a parser like Docparser, is that it’s convenient, quick, and accessible to users without technical expertise. The tradeoff of this kind of lightweight solution is that it is often limited in function or capacity, and loses in accuracy and quality what it makes up for in convenience.

Adobe Acrobat

Using Adobe Acrobat, converting a PDF to Excel is done by exporting the document and selecting “Microsoft Excel Workbook” as your destination filetype. From there, it’s as easy as saving the file and getting to work.

Acrobat is the solution best equipped to preserve the precise formatting of your source PDF, including images, colors, and tabular data structures. However, the program tends to misinterpret more complicated formatting, and might require heavy manual adjustment after the fact.

Copying and pasting

Believe it or not, this is still among the most used approaches to pulling data from PDFs. This approach is far and away the least automated, retaining the greatest human control, and thus the greatest potential for human error.

PDF parsing with Parabola

Fortunately for those working in operations and logistics today, advancements in AI have made tooling for PDF processing far more robust than previous ineffective or high-code approaches.

The advantage of a tool like Parabola is that it is able to ingest PDFs at scale, and with a great degree of customization and accessibility. The key lies in combining optical character recognition (OCR) vision technology with state-of-the-art large language models (LLMs).

On the front end, says Reisfield, you don’t need any technical knowledge, just the ability to tell Parabola in natural language what type of document it’s looking at, and how you want the tool to read it. Equipped with this natural language prompt, Parabola uses LLMs on the backend to interpret the user request, and applies OCR tech to read the document as a human would.

According to Sanchez, this means that Parabola can be “as flexible as a person reading each PDF individually, but as precise and efficient as a computer parsing massive quantities of documents automatically.”

And not only does this technology excel at extracting data quickly and accurately, Parabola is also uniquely positioned to help you get that data where it needs to go. Says Reisfield, “Even the tools that are the best in the world at pulling information off of PDFs — they’re almost never also best-in-class logic builders that have integrations with every tool in your stack.”

Choosing your path forward

Think of PDF parsing tools as keys to your digital vault. The right key depends on your specific needs — whether you're handling a few documents or processing thousands; working with simple forms or complex layouts. By matching your requirements to the appropriate solution, you can transform PDF processing from a bottleneck into a streamlined part of your workflow.

Consider your organization's use cases, volume requirements, and technical resources when choosing between lightweight online converters, or automated solutions with built-in integrations. The right choice will unlock not just your PDFs, but your team's productivity as well.