5 Ways that Cognitive Document Processing is Disruptive

See How Cognitive Capture is Disrupting Legacy Document Processing

When you think about artificial intelligence and data integration, what's the first thing that comes to mind?

Maybe deep learning neural nets crunching away at big data sets?

Or aggregating data from dozens or hundreds of repositories and streaming it into analytics or business intelligence platforms? Or maybe predictive healthcare and extending human life?

I'm willing to bet you didn't think about document data integration. It's a challenge that the enterprise has always struggled with. Until now, solving the problem of integrating unstructured data from documents has been a very painful experience.

But that has changed. Intelligent document processing platforms now offer a streamlined approach that produce real results. The process isn't easy or for the faint of heart, but the technology has finally arrived. 

But why now? What's the difference between long-standing capture tools and this new breed of cognitive document processing?

What is Cognitive Document Processing?

Cognitive document processing is a platform or software that leverages cognitive abilities like machine learning and artificial intelligence in order to make it easier and faster to process documents and extract the data out of them.

This form of document processing decreases operating costs, enhances customer and employee satisfaction, and makes it easier to stay compliant with regulations.

Get Our Free Guide
to Maximize Your Document Processing!

Did you know that most document processing solutions out there can't handle your everyday document data tasks? It is tough to know what technologies you need to get great processing.

But this guide is here to help you understand what makes the best document processing software, and why. Get the information-packed, 6-page guide:

5 Core Innovations of Cognitive Document Processing That are Disrupting Work:

1. New computer vision techniques help machines more than ever
2. Visualized machine learning simplifies things
3. Several enhancements maximizing OCR
4. Fuzzy regular expression = easier data discovery
5. Classification engines help machines understand data in new ways

Many industries rely on documents for important workflows:

• In healthcare, large text files communicate explanation of benefits, email contains retrospective denials. Then there's claims, and the list goes on and on.
• In manufacturing and supply chain, there's an endless amount of logistics documents.
• Legal runs on paper, and so does the oilfield.
• Education processes transcripts at a dizzying rate.
• Financial institutions process everything from mortgage and loan documents to personal checks.
• And you know that Government loves its forms...

Organizations in nearly every industry are saturated with paper, and storing millions of archived records (and all this data is a literal gold mine).

Tech Finally Catches Up to Paper

But why the renewed focus on documents? The reason is that technology has finally caught up. For decades, data contained on paper has been extremely difficult to integrate.

While it's true that tools have existed for setting up rigid templates that "know" where certain data is on a document, their use is extremely limited.

In the real world, these templates have caused a lot of suffering because of how fragile they are. If a word or number is just outside of where the template is looking, another template must be created to find it. This is hardly scalable.

Cognitive Document Processing: The 5 Critical Features

1. New Computer Vision Techniques Help Machines More than Ever

Computer vision (CV) is the technology responsible for making scanned documents machine-readable. While all non-text artifacts on a document are no problem for a human to read past, they cause many problems for machines.

Humans understand that a hole punch is not a word, and stamps, lines, barcodes, and images are all just there to support the intent of the document.

But these non-text elements cause big problems for optical character recognition.

Improved OCR Accuracy and Handwriting Recognition

OCR is only as good as the document image it runs on. Modern analytics and business intelligence platforms (and neural nets) all require very accurate (and labeled) data. Traditional OCR's low accuracy doesn't produce acceptable data. This is one of the reasons quality cognitive document processing has been difficult to achieve.

New CV algorithms paired with advanced hardware acceleration enables near-100% OCR accuracy using both new and traditional OCR engines.

And handwriting? New advances in computer vision now enable robust handwriting recognition that streams even more information from documents. 

2. Visualized Machine Learning Simplifies Things

A new approach to machine learning and classification sheds light on the complicated algorithms doing the heavy lifting. Solutions using this tech provide a user interface which reveals trained data in a way that is easy to understand. This visualization framework automates human understanding of otherwise hidden algorithms.

The design philosophy behind this approach is that subject matter experts understand their data better than anyone else. As a result, automating their understanding of how A.I. is operating is both easier and achieves better results than a "dark" machine learning model.

This kind of transparency is based on the belief that a subject matter expert will always be able to make better decisions on data than "hidden" A.I.

3. Several Enhancements Maximizing OCR

As previously mentioned, traditional OCR engines need help for maximum performance. Several key OCR innovations are at the core of  AI document processing platforms:

4. Fuzzy Regular Expression = Easier Data Discovery

Regular expressions (RegEx) have been used to process text since the 1950's.

Modern data science tools have enabled a new kind of RegEx that allows for less literal character matches. In fact, Fuzzy RegEx enables true machine reading by providing a more organic understanding of text.

The way this innovation works is by "fuzzy matching" results to lexicons and external data sources by using weighted accuracy thresholds. Machines now return results that are "close to" what a user is searching for and that is extremely valuable in discovering data.

5. Classification Engines Help Machines Understand in New Ways

Automating document classification is a critical step for accurate data integration.

In many real-world scenarios, documents are not always stored in the proper sequence, or manually separated by type. Humans have no problem looking at a document and understanding the context of the information.

BUT, if we expect a machine to read and integrate data from documents, creating an understanding of the intent of the document is necessary.

Classification engines use machine learning or rules-based logic to recognize and assign a document type to a page, or a group of pages in a document. Here are three types of classification techniques:

Advances in machine learning enable users to train cognitive document processing systems in a visual interface to see exactly how the machine is learning. This makes classifying new document types or troubleshooting problems extremely easy.

This article was updated 12/2/2020.