5 Trends in OCR Accuracy for Data Extraction

OCR, AI OCR, or Intelligent Document Processing (IDP) are data extraction solutions from documents. OCR is used for simple documents, while IDP can handle a greater variety of documents, from simple to complex, unstructured documents. 

Accuracy is a key measure of how these systems perform. We can think of data extraction accuracy as a system’s ability to extract information from a document without error.  Data extraction accuracy enables a system to meaningfully and speedily power its AI with correct information. 

Understand the different, and important, categories of accuracy in our blog here.

Here are 5 trends around accuracy in today’s marketplace, and why they matter to you:

1.) All eyes are on extraction accuracy Accuracy is enterprises’ number one question for analysts. 

Accuracy is enterprises’ number one question for analysts. 

Buyers are asking what accuracy they can expect from various solutions because it’s at the top of their minds. 

There’s no other good way to evaluate vendor claims around accuracy. And it’s too important to the bottom line not to know. 

Tackle that trend:  Accuracy is a keystone KPI because it impacts so much. It affects your planning -- you need to plan for manual corrections and extraction, and to know your total cost to process a document. To better understand how accuracy impacts your automation business case, we recommend you use a Five Why's strategy to uncover the thorough about accuracy. 

Check out our blog post on why you should aim for 100% accuracy.

2) There’s a lot of smoke and mirrors 

Vendors may make claims on their websites about the accuracy, but they aren’t telling you how accurate their solutions actually are. 

That lack of transparency represents an extra risk to you. 

Vendors don’t want to promise you an accuracy they might not be able to deliver. Their guesses and vague language represent a lack of confidence that their technology can deliver the results you need. 

Tackle that trend: Read the fine print, and make sure you understand the type of document the vendors can process. Make sure their technology applies to your use case, and that they can keep accuracy consistent over time as documents change. Cut through the smoke by asking them to make you an accuracy guarantee.

3) Zooming in on business outcomes 

Automation allows businesses to generate better business outcomes, and accuracy serves as a mile marker for how well it’s working. 

Clients want their end-to-end processes to perform well and deliver value to their customers.  Accuracy is a proxy for the limitation data extraction will put on the end-to-end business process and what it can deliver. The higher the accuracy, the better the process performance. 

Tackle that trend: Start with your desired business outcomes, then work backward to find your required automated data accuracy. Let’s say your automation solution provides you with 60% accuracy (be sure you know if the answer is a document or field-level accuracy). 

That means you have to manually process 40% of the documents. Understand how this automation-human ratio impacts your business outcomes, and think about what netting that other 40% data accuracy could mean to your business. What would 100% data accuracy help you achieve?

4) Tap dancing around definitions

There’s no standard definition for accuracy, so vendors often just use the definition that works best for them. 

Accuracy is a critical indicator of process efficiencies, so it’s crucial that you understand how your vendor means it. That said, if everyone’s using it differently, it becomes difficult to compare vendors. 

Tackle that trend: Find out what is being measured and where, and how their definition of accuracy will impact your use case. Keep those questions coming until you’re convinced you’re working with someone you can trust. 

5) Higher accuracy is possible 

Properly used, AI will power your solution to new accuracy levels. However, the trend toward AI-based OCR solutions still hides important nuances of accuracy that can impact your business. 

AI-based extraction is a powerful tool and can provide better performance than OCR.

But AI typically will start with low accuracy and, as it learns, develops high accuracy. So an AI-based solution might start off with lower accuracy than an OCR but over improve very quickly as it sees the real documents. 

It is easy to get stuck in an “AI vs uncertainty” mindset. Yes, your use case requires AI, but is AI going to deliver?  How do you manage AI risks?

Tackle that trend: The future is AI and it can help you automate data extraction from documents. Make sure the application of AI for automation works for your use case and for your document types. While an AI OCR might slice high accuracy on invoices, it may deliver poor accuracy on loan documents. 

Download the 6 AI Risks eBook here.

AI OCR Accuracy and STP

Accuracy is a leading indicator of how well your business will perform once data extraction is automated. The higher the automation accuracy the better the performance. 

Many firms we talk with strive for straight-through processing or STP.  STP is when the automated extraction solution performs so well that it delivers 100% accuracy.  STP is a touchless approach to extraction:  It’s 100% automation with 100% accuracy. 

When STP is achieved, process magic can happen. It is a holy grail of processing through deep learning ORC. 

AI OCR Accuracy Trends

Accuracy is an important topic to understand when automating data extraction from documents.  Yet there is significant uncertainty around what accuracy is and what can be achieved for your use case.

When it comes to accuracy, Infrrd believes the answer to “what’s my accuracy?” should be something you can bank on.

Accuracy uncertainty should not be a roadblock to your success.