Looking at the results of a recent blood test got me to thinking about the difficulty of extracting data from PDFs.

Here’s an Example of Tough-to-Extract (and Tough-to-Understand) Data from PDF Files

Ahead of an upcoming annual health checkup, I completed a blood test. Eager to see the results, I downloaded the test data as a PDF document from the new fancy web portal my provider offers. And wouldn’t you know — it was not easy to read.

As a data professional, I know good labeled data when I see it, and this sure wasn’t it.

There seems to be a misconception in the marketplace. People think that a low code / no code platform means the technology is easy to master.

Or some think that low / no code means low or no training and that’s just not the case. Take in case certain RPA vendors who have nearly a hundred training classes. Why so many?

Difficult vs Complicated

Intelligent document processing (IDP) is often confused with optical character recognition (OCR) because they both aim to achieve the same general goal: machine reading.

IDP is different than OCR because it is a software platform that combines multiple tools and technologies to process data. OCR is a single tool that converts pixels to characters and has minimal effectiveness on its own.

Today’s world is a sea of information. What’s the relationship between the way humans navigate it all and the way we build A.I. to do the same thing?

This article explores the way human decisions shape knowledge and beliefs, and why we believe things that aren’t true.

First, Think About How Babies Learn

If we learn how human beliefs are formed, it is that understanding that must be used to build better, and more ethical A.I. Otherwise, we risk building in dangerous bias from the get-go.

How does AI learn? It’s a question often asked but rarely is the truth revealed. AI is just not as smart as we’d like for it to be.

But let’s not confuse smart with powerful. Or, power with compute. At the end of the day, all we really want is for technology to do difficult things quickly and with very little human effort. We also want these things done accurately.

I’ve often wondered why we haven’t gone back to the moon. After a little research into the topic, I immediately saw similarities with automated intelligence projects.

But first, the moon.

It was July of 1969, when one of the most historic events in human history took place — humans walked on the moon.

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No matter where you’re at on your automation journey, at some point difficult data throws a wrench in the gears. Even very large automation projects with seemingly straight-forward requirements are halted with just a single pesky data source that seems untouchable.

The good news is that no data is out of reach, and if you are looking into intelligent document processing, this buyer’s guide will provide the resources you need to make the best decision possible.

This guide will include pricing as examples to help you ballpark pricing expectations.

Intelligent Document Processing Buyer’s Guide Table of Contents:

Disclaimer: Before I answer the question “What is intelligent document processing,”…

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As humans, we love the tales of humanity’s triumphs over the machine because they reveal our need to deal with the fear of being replaced.

When new technology comes out that threatens my, or your job security, it’s only natural to detest the machine and prove it’s not more capable, or to find it’s weakness and sucker-punch it (imagine AI writing my articles…no thank you!).

But, this is all wrong… Seeing artificial intelligence as a threat is a gut response that limits our ability to thrive and conquer in whatever era we live in.

Touting AI as a Way to Replace Human Workers is a Fallacy in Thinking

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Automated document processing software is the next generation of capture that combines new technology like computer vision, machine learning, and natural language processing with traditional OCR tools.

Instead of data science methods like natural language processing being an add-on (or an afterthought), they are worked into every aspect of how the software works.

This — and changes to the way software is architected to take advantage of modern compute and data storage — makes today’s automated document processing software worth taking a deeper look at…

The 6 Fundamental Technologies in Automated Document Processing Software

  1. Computer Vision
  2. Optical Character Recognition
  3. Natural Language Processing
  4. Machine Learning
  5. Data Extraction
  6. Data Integration

1. Computer Vision


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The intelligent automation market is seeing massive growth year over year. As it’s often said “You can have accuracy, speed, or low costs, but you only get to choose one.” Is the mantra still true today?

There are automation solutions for everything — home automation, industrial automation, process automation, task automation, driving automation, and even a fully automated robotic kitchen (just don’t get in its way…).

The success of intelligent automation is more than product selection. The way you think about human and machine interaction will determine overall accuracy and the business impact you will achieve.

What is Intelligent Automation?

Intelligent automation is…

Jesse Spencer-Davenport

I enjoy solving problems through business process analysis and increasing revenues through excellent content marketing.

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