How to Extract Data from PDF to Structured Data

Learn more about the common use cases of PDF data extraction and how you can extract data from PDF files with manual or automated methods.

Published on:
January 5, 2022
Last updated:
November 21, 2022

Most businesses today store large amount of information in PDF files. While PDFs are convenient for storing and sharing data, they can be difficult to work with, especially when it comes to extracting data for further analysis. Fortunately, there are a number of ways to extract data from PDFs and convert it into a structured format. In this article, we'll explore a few different methods for extracting data from PDFs so that you can get the most out of your data.

What Is PDF and Why Do So Many Businesses Use It?

PDF, or Portable Document Format, is a type of file used to present documents in a way that is independent of application software, hardware, and operating systems. PDF files include text, images, and other data that can be viewed on various devices, from personal computers to smartphones. PDFs can be created from scratch or converted from other files, such as Word documents or images. In addition, PDFs can be password-protected and can contain fillable form fields. As a result, PDFs are a versatile and popular format for business use.

Businesses use PDFs for all sorts of things. For example, you might use a PDF to send a contract to a client. Or, if you're selling a product online, you might use a PDF to give customers a product manual. You can also use PDFs to create printables, like planners or worksheets. A PDF is a good option if you need to give someone a document they can read (and maybe print out).

Why Is It Hard to Extract Data From PDFs?

There are a few challenges that can make the process difficult. First of all, PDF files cannot be edited, meaning that any data you want to extract must be manually copied and pasted. This can be time-consuming and tedious, particularly if the PDF file is large or complex.

Additionally, when you convert a PDF file into another format (such as DOC or CSV), the original formatting and order are often lost. This can make it difficult to work with the data afterward. Finally, PDF files are often not structured in a way that makes it easy to extract specific information. For example, PDFs often contain images of scanned documents, which can make extracting information even more challenging. As a result, extracting data from a PDF can be a time-consuming and frustrating process.

Common Use Cases for Extracting Data From PDF Files

Depending on the business, common use cases for extracting data from PDF files can vary. However, some business documents are commonly used across different types of businesses. Here are a few common use cases where extracting data from PDF files can be helpful.

Receipts: To keep track of business expenses, many companies require employees to submit digital copies of their receipts. Information like date, name of the product, and total amount can be extracted from PDF receipts to automatically populate an expense report.

ID Documents: When onboarding new employees or clients, businesses will often need to collect information such as their full name, date of birth, and address. This data can be extracted from a scanned ID document.

Business Certificates: Companies may need to provide proof of their business registration or licensing to bid on projects or apply for loans. This information is typically available in a PDF document from the relevant governmental body.

Address Proof: Many businesses require customers to provide proof of their current address when opening an account or making a purchase. This data can be extracted from a PDF copy of a utility bill, bank statement or other official document.

Purchase Orders: To keep track of inventory levels, businesses need to know when new shipments are delivered. You can extract data such as the expected delivery date and the list of products ordered from a PDF purchase order.

Finance & Banking

Extracting data from PDF files is a common requirement in many businesses, especially those in the financial and banking industries. For example, banks and insurance companies need to be able to capture data from passports, IDs, business certificates, and proof of address documents on a daily basis. However, these documents are often scanned in picture form and then converted into PDF files, which can present a challenge for financial institutions when it comes to extracting the data.

In many cases, the images will need to be manually reviewed to identify the relevant information. This can be a time-consuming and labor-intensive process. However, some advanced PDF software solutions can automate the data extraction process, making it much faster and easier. As a result, these solutions can save businesses a significant amount of time and money.

Public Sector

In public sectors, extracting data from PDF files happens every day. As this sector deals with hundreds or even thousands of citizens waiting for services, efficiency becomes the top priority. The data might be in the form of application forms, birth certificates, ID cards, or any other government-issued document. The process of extracting this data is manual and time-consuming, often causing delays in service delivery.

In addition, there is a risk of human error, which can lead to inaccuracies in the data. This can create problems down the line, especially if the data is being used for decision-making purposes. To overcome these challenges, public sectors are increasingly turning to automated data extraction solutions. These solutions can quickly and accurately extract data from PDF files without requiring manual input. This saves time and resources and reduces the risk of errors.

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Different Methods to Extract Data from PDF

1. Building an In-House Team

Building an in-house team may be the best option for companies worried about security or quality control issues. While some upfront costs will be associated with hiring and training new employees, businesses will have greater control over who has access to their data.

In addition, they’ll be able to ensure that their team members are trained in data entry procedures. However, there are also some drawbacks to this approach. In-house teams can be more expensive than outsourcing and may require more management oversight than third-party contractors.

2. Outsourcing Manual Data Entry

Several online services, such as Upwork, Freelancer, and Fiverr, allow businesses to post data entry projects and receive bids from a pool of potential workers. The major advantage of this approach is cost. Data entry services are typically much cheaper than hiring full-time employees to do the same work. In addition, it can free up time for more skilled workers to focus on other tasks.

However, there are also some significant disadvantages to outsourcing data entry. Quality control can be an issue, as businesses have little control over the workers they hire. In addition, there is a risk of security breaches if sensitive data falls into the wrong hands. As a result, businesses should weigh the pros and cons carefully before deciding whether or not to outsource these tasks.

3. PDF Converters / OCR Engines

Adobe Acrobat is one of the most popular PDF converters that help convert PDF to Excel. This tool allows you to select the entire document or just a certain section you want to convert. The downside to using Adobe Acrobat is that it can be a bit pricey.

If you're looking for a free PDF converter, SmallPDF is a good option. This converter offers many of the same features as the paid converters mentioned above. However, there are some limitations to using SmallPDF. For example, it doesn't support as many file types as other converters on this list.

Another popular method to convert PDFs is using OCR (Optical Character Recognition) engines. This process involves using an OCR engine to scan and identify the text within a PDF document. You can extract the texts from PDF files and convert them into machine-encoded texts.

The thing is, though, these tools can be slow and may not be able to process large files. Additionally, PDF converters and OCR engines themselves aren’t capable of outputing structured data, making it difficult to work with the information you've extracted.

Let's take a look at the example below. This is a sample purchase order from TemplateLab

If you attempt to extract information from this image using an OCR engine, the result might look something like this:

\/(\& Purch OrdTemplateLABFASHION QUEEN PURCHASE ORDER #:  123456/22(000) 1234 56789CLIENT INFORMATIONName: FASHION ITEMS INC _ Attn. Sam Martin (Chief of Finance)Address: _ 211 Arrow Bay, Westminster, 21656 Los AngelesEmail: Phone: (555) 1234 56789ORDER INFORMATION[romtaos v v |+ | w | s | o)Comerwrmsmmos s | o | [ | our | o]oo omowe [ | o [ |+ | soem)oo own [y |+ | | i | eSHIPPING INFO PAYMENT INFOMethod: Courier Subtotal: $3,565.00 Discount: _10%Company: _ FedEx Taxes: _10% Shipping: _§75.00Track #: 1222052520000680 Payment Type: Credit CardArrival Date: 29/09/2022 TOTAL: _$3,604.35NOTES

Although the OCR engine has helped you extracted the texts, you still have to re-organize them into a structured format for further processing; however, this is still faster than completely relying on manual data entry.

4. Automated Data Extraction From PDF to Structured Data With IDP

IDP (Intelligent Document Processing) is a technology that can be used to automatically extract data from batches of images of PDF files. The IDP process typically involves four steps: Pre-Processing, Extraction, Validation, and Post-Processing.

  • In the Pre-Processing stage, the IDP solution will optimize the images for light condition, skew correction, and other aspects since images come in different conditions. Pre-processing these images maximizes the accuracy of extraction.
  • In the Extraction stage, the machine learning models then identify the data that we want to extract and then return them as key-value pairs.
  • In the Validation stage, the extracted data is checked for accuracy and completeness.
  • In the Post-Processing stage, the extracted data is formatted and outputted in the desired format.

The main advantages of using IDP for data extraction are its accuracy and reliability. IDP solutions can achieve high levels of accuracy because they utilize various image processing techniques. Additionally, IDP solutions are designed to be scalable and flexible, making them well-suited for extracting data from large batches of PDF files.

However, one potential downside of using IDP for data extraction is that it can be costly when demand is low. However, FormX provides flexible and scalable billing options that make it easy to downscale your IDP solution when demand is low.

There you have it! These are just a few of the many different methods that you can use to extract data from PDF documents. As you can see, each method has its own unique advantages and disadvantages. Ultimately, the best method for you will depend on your specific needs and preferences. If you're looking for a quick and easy way to get started, FormX Automated Data Extraction is a great option. With FormX, you can easily extract data from all kinds of PDF documents; moreover, you can even train your own custom extraction models with a simple 3-step process.

Sign up for a free trial or contact us so that we can learn more about your use cases and how FormX can be of help to you.

Extract data from these documents
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Purchase Orders
Bank Statements
Contracts & Agreements
HR Forms & Applications
Shipping Orders & Delivery Notes
Loyalty Members Applications
Annual Reports
Business Certificates
Personnel Licenses
And much more!