Automate PDF Data Extraction for Faster Insights

By | 2020-05-14T01:46:30+00:00 May 14th, 2020|

PDF (Portable Document Format) is an industry-standard and one of the most widely used formats for presenting and exchanging information. Some common business documents that are shared in PDF format include invoices, purchase orders, and contracts.

However, while PDFs are great for exchanging information, it can be quite difficult and tedious to extract valuable insights from these files. This is because the data stored in PDF files is unstructured and can contain a variety of different data types (including text and images). The process becomes even more challenging when you have to do it manually for each PDF file.

Automating PDF Data Extraction - The What, Why, and How

This blog post will discuss why it essential to automate PDF data extraction and how it can help businesses grow.

Manual PDF Data Extraction

The process of manually extracting information from PDFs is resource-intensive. It requires someone in the team to manually rekey all the information in the PDF document, leading to errors and long turnover times.

This entire process becomes cumbersome when a batch of hundreds of PDF documents is involved. Even if you have multiple resources for data retrieval, it can take days or even weeks to get actionable information.

Let’s break it down in numbers to help you understand the cost incurred by manual data extraction. Imagine you have a dedicated analyst onboard responsible for extracting data from PDF documents and analyzing it.

  • The average salary of an analyst = 60,000 USD per year (US median wage)
  • Average time spent by an analyst on data extraction, cleaning, and preparation per day = 70%
  • Cost incurred by an analyst in PDF data extraction and preparation = $42,000

With manual data extraction, the majority of the time and effort of the resource is spent on preparing data rather than analyzing it.

An alternate approach to this can be to outsource data extraction entirely, but this has its own drawbacks since it incurs a high recurring cost, and you may not want to share all your crucial business documents with a third-party provider.

All in all, manual extraction is not just time-consuming, but it is an inaccurate and expensive endeavor as well. Another time-and-cost effective solution is using an enterprise-grade data extraction tool, such as Astera ReportMiner, and automate the PDF data extraction process. Such tools reduce the manual effort involved in the extraction process, speed up data availability, and ensure data accuracy.

Importance of Automating Data Extraction for PDF Documents

Keeping the challenges of manual data extraction in mind, a desirable solution for businesses is to be able to parse all kinds of PDF documents with minimum human intervention, through third-party tools. Here’s how these tools can help your businesses:

  • You can create and configure rules and formulas to be used to extract the data from PDF files. This reduces the time needed to search and copy/rekey the required information manually.
  • You can convert images into text through the use of OCR engines without having to type the data again manually. This reduces the probability of typos and other errors during extraction.
  • You can automate the entire extraction pipeline and run it on a batch of PDF files to get all desired information in one go. This improves business efficiency and ensures that the data is available as and when needed.

How to Automate PDF Data Extraction?

You can automate PDF data extraction using one of these two methods:

  1. Writing code or scripts for document processing that can extract the desired information from PDF documents. This is not recommended for most businesses because it involves high complexity, dedicated developer resources, and it often requires you to rewrite/modify code whenever the document structure changes.
  2. Make use of a data extraction solution, such as ReportMiner, that comes with built-in support for creating extraction templates and provides an easy-to-use user-interface that involves zero codings. This is recommended for businesses that need to extract information quickly and accurately from high volumes of PDFs.

Here is how you can automate data extraction from different types of PDFs using ReportMiner:

  • Text-based PDFs: For text-based PDFs, you can create an extraction template consisting of data regions and fields (sections and values that you want to extract) through which ReportMiner can read these documents and retrieve information.
  • Scanned (image-based) PDFs: Not all PDFs consist of text data, most PDF documents that businesses deal with consist of scanned images (e.g. invoices). For this, ReportMinner’s OCR (optical character recognition) capability can extract text data from images. Once you have run your scanned PDF through ReportMiner, it becomes similar to a text-based PDF and simplifies information capture.
  • Form-based PDFs: Often, businesses have to deal with PDF forms, such as customer surveys or employee feedback forms. These PDFs are more structured than other types so you can make use of ReportMiner to extract key business data (such as customer information) and use it for reporting and analysis.

Once you design an extraction template in ReportMiner, you can reuse it to automate extraction from PDFs with similar layouts. The tool allows you to read files from disparate sources, including the FTP server, email server, and unstructured files.

The extracted data can be further massaged and exported to a destination of your choice. Some popular options include Excel, databases, and .CSV file.

automated pdf data extraction


Businesses capture and deal with a variety of information in PDF documents, including transactional and reporting data. The challenge lies in extracting and structuring this information with reasonable accuracy and speed. This can be achieved by automating PDF data extraction through ReportMiner.

To experience first-hand how Astera ReportMiner can help you liberate data from PDF files, download the trial version.