PDF Scraping: How to Extract Unstructured Data from PDFs?

Even as businesses let go of most paper processes, PDFs remain the default format to exchange information with. It makes sense. Not only are PDFs compatible with all platforms and operating systems but most importantly, they’re secure.

Businesses can protect PDFs with a password and know that when the document is opened, important data is unlikely to be changed due to a compatibility issue. Nonetheless, PDFs do have one major issue: they are an unstructured form of data exchange. PDFs don’t have a standard format and as a result, extracting data from them isn’t always straightforward.

That's where PDF scraping comes in. In this article, we'll be discussing why PDF scraping is important, what the challenges of PDF scraping are, and how to scrape/extract data from PDFs.

If you’ve never heard of the term before, PDF scraping simply refers to the act of “scraping” or extracting data from PDFs. Businesses have to extract data from PDFs in the first place because of two things: the format of a PDF and the value of data.

As mentioned, PDFs are an unstructured form of data. This is quite common. Unstructured data accounts for about 80% to 90% of data generated and collected by businesses. The challenge that this creates, however, is that the information they contain cannot be processed by software for further analysis. Well, not unless the data is extracted first.

PDFs are used to exchange all manner of business documents such as bank statements, invoices, and receipts. The information in those documents is valuable but can only be processed by software if it’s extracted and placed into structured formats. A PDF on its own is just a flat document for humans to read but PDF scraping ensures that the data on it can become multi-dimensional in use.

To be processed directly by data software and understood programmatically, PDFs would need some kind of markup or hierarchy of data. They tend to lack both these things, which is why many businesses have resorted to simply extracting data from PDFs manually.

Manual data entry comes with its own issues though. Whether it’s performed internally or outsourced, it can be time-consuming and costly. Errors are a far greater risk, which may go on to cost a business unnecessary money and time. If a business is receiving hundreds or even thousands of PDFs a day, it’s also by no means an efficient or sustainable way to extract data.

The next step is then ideally building software that could extract the data from the PDFs and enter it into a data processing program. What makes PDF data scraping difficult is what always makes PDFs tricky: they come in a range of layouts and formats. Any software tasked with extracting data from them would need to understand the context of the document and then locate the exact data fields.

The software would also need to be easily integrated with whatever is meant to be processing and analyzing the data so as not to bring the whole workflow to a grinding halt. If the PDFs are image-based, extracting data is even more complicated and would require OCR incorporated into the software. Something that could scrape from PDFs and make information accessible would improve so many everyday business operations.

It’s why we’ve developed something that does exactly that, but we’ll get there.

When it comes to extracting data from PDFs, there are a few options that you may be considering. Let’s take a closer look:

By far the most tedious, manual data entry comes with problems no matter how you approach it. Typing each value from a PDF into a spreadsheet is time-consuming and very easy to mess up with just a single typo.

Copying and pasting the information into another document format is another way to manually scrape from PDF. Again though, there’s a risk of errors being made as the formatting and order of the information is likely to get muddled as it gets copied over.

A PDF converter typically just converts text-based or image-based PDFs to machine-encoded texts. The end results however are often not structured and will still need to be processed further in order to make the data usable.

PDF converters shorten the process of extracting data from a PDF but they’re still not an effective means of streamlining data extraction. They’re a form of pdf data scraping that simply gets the information out of the PDF without actually readying it for data software.

A PDF scraper or Intelligent Document Processing solution like FormX will not only automate data extraction from PDFs but work with other data software to ensure that the information is delivered directly into processing and analysis.

By integrating different technologies, including OCR, machine learning, and image optimization, PDF scrapers like FormX can “read” PDFs, extract the necessary information and deliver it as structured data, often in the form of JSON or CSV. This is why PDF data scraping is so effective at streamlining business workflows.

Here’s a step-by-step process of how an Automated PDF Extractor works:

1. Collecting Samples

The first step involves collecting samples of PDF documents that will serve as training sets for your extraction process. These samples play a vital role in ensuring the optimal performance and high accuracy of the extraction models (extractor).

2. Training Your Extractor

The collected samples are then utilized to train extractors according to your specific needs. The more samples you feed to the extractor, the better the performance in accurately identifying and extracting data from PDFs.

3. Test and Verify

To ensure high accuracy of data extraction, you can test out your extractors with sample images with different layouts. Manual validation is essential during this phase, as it helps identify any errors or inconsistencies that the extractor might overlook.

The beauty of having extractors built upon machine learning algorithms is that the accuracy can be increased by feeding the extractors with more sample images and labeling the relevant data fields of the sample images.

4. Data Extraction and Processing

Once the trained extractor is validated and refined, you can now use your extractor to extract data from a large batch of PDF documents efficiently and accurately.

The extracted data will then be transformed into a structured format such as XML, CSV, JSON or other standardized formats that can be directly imported into different software. Afterward, the structured data will be seamlessly fed into your databases, spreadsheets, or analytical tools for further insights and decision-making.

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The usefulness of PDF scraping across industries and businesses is undeniable. Not only does it take a fraction of the time manual data extraction would take, but there’s also less chance of silly, costly errors being made.

The most common uses that we see are for document verification or customer onboarding with identity documents and proof of address. It also has great uses in finance and accounting by streamlining how information is extracted from receipts and invoices. In fact, PDF scraping can help automate the entire accounts payable system.

Any accounts department knows the headache of receiving invoices in a multitude of formats. It doesn’t matter if they’re all PDFs, extracting the data is no less time-consuming. PDF scraping that incorporates machine learning and OCR however is equipped to read the documents, regardless of the format, and automate the extraction of that data.

Identity documents and passports, with all their variations, are often sent as scanned copies in PDF form and can be a headache to go through manually. There again, a PDF scraper like what we offer at FormX is able to go through the document and take exactly the information a business needs and place it into a usable file type.

All those moments when someone is forced to pause and scan their eyes through a PDF to find the necessary information and then type it up are saved by simply automating pdf data scraping.

Though smaller businesses might be fine with manual data extraction from PDFs, as businesses scale up, they need a system that can be scaled with them. Manual data entry just isn’t sustainable when hundreds of PDFs are having to be processed each day in multiple formats and with different end-uses.

An intelligent solution like FormX, which integrates OCR technology, is scalable and far more accurate and efficient than manual data entry or even using PDF converters. The accuracy that we can offer is also far more cost-effective in the long term. Mistakes cost a business. FormX PDF data scraping helps to limit that from happening.

FormX has a set of pre-trained templates that cover an array of uses such as extracting data from receipts, identity documents, business registration, and more. Our software reads the PDF that you upload, extracts the data, and then makes it available in a structured format as a JSON or CSV file that can easily be used by other systems for further analysis.

All of our sets can be integrated with API to help your business batch process PDFs and return CSV files in a fraction of the time it would have taken someone to do the work manually.

Our templates mean that you don’t need to write any code, we’ve already ensured that the software is ready to automate the most frequent tasks that businesses are covering each day. If however you have other types of PDF files to process that aren’t currently covered by our sets, it’s a very simple adjustment.

Samples of the documents just need to be collected to train the software, after which the only thing left to do would be to test and verify the data extracted from the PDF files. The great thing about the solutions that we offer at FormX is that they are intelligent not only in how they make use of AI technology such as OCR, but in how adaptive they are.

We understand that PDFs are unstructured data and that most of what businesses are trying to deal with each day is unstructured too. Receipts may be top of mind today but tomorrow proof of address may be an issue. We simply provide software that can automate and streamline how data is extracted so that businesses can save time and money, regardless of context or even country.

If you’d like to see FormX in action, check out our use cases here or talk with us directly to see how we can make your workflow more efficient. If it sounds simple, it’s because it is.