Traditional OCR vs AI: OCR accuracy matters

Imitating human behavior increases OCR accuracy

Data capture for invoices ought to have been solved a long time ago! That’s what most people think, and that’s what we thought too. When we started talking to customers, we realized the reality is different. Learn how data capture is made even more effective with the ultimate in OCR accuracy.

OCR accuracy

Businesses today have so much information — such as accounts payable (AP) invoices — to process, and unfortunately, many companies are still using manual data processing methods which can slow down operations and increase costs for the company overall. So, how can your business speed up this process and lower the costs of your data processing system? 

As technology advances and more software options are becoming available on the market, there has been an increase in the number of automated information extraction technologies. 

There are two types of automated optical character recognition (OCR) solutions — template-based and cognitive — that businesses can choose from when looking for a system to speed up their accounts payable process.

As technology continues to evolve, you can improve OCR accuracy with advanced image preprocessing software solutions to create an even more effective system for processing your invoices and help to grow your business without increasing your costs exponentially.

So, what is OCR accuracy exactly? The accuracy of OCR technology is rather dependent on which type of OCR system your company is using to process your incoming accounts payable invoices. 

For example, if you’re using a traditional template-based OCR solution, you will likely be able to cut down on some of the time it takes to process your invoices. Additionally, you can automate some of the monotonous and draining tasks so that your accounts payable team can focus on other more important tasks. 

The downside of these template-based solutions is that for each new invoice layout you will need to create new templates and rules to properly process the information. So while you are eliminating some of the manual tasks your AP team does, there is still a level of manual processes that go with maintaining and creating rules and templates for each type of document you are processing.

Cognitive OCR technology, on the other hand, uses machine learning (ML) and artificial intelligence (AI) technologies to actually understand the data it is processing. Just like a person, cognitive OCR — also called cognitive document data capture — gets better at recognizing and capturing the important information as it sees more document layouts. 

In other words, this type of OCR technology gets more accurate with time and learns how to be more effective with use. Because of this, cognitive OCR solutions can be used to fully automate your data entry processes — without any need for setting up templates for each new document layout. 

This being said, it is always a good idea to keep a human in the loop to monitor and ensure accuracy and keep the system running smoothly.

OCR engines

There are a number of OCR software solutions available online for businesses to use to better streamline and process their incoming data. But this hasn’t always been the case.

Before other, more advanced solutions — such as Rossum’s cognitive OCR software — became available, many OCR developers used Pytesseract (or Python-Tesseract) which is an open-source OCR Python tool. 

This tool is a template-based OCR system and as such can be quite painful to continue modifying to accept new document formats. This is especially true as more and more businesses are joining the digital market and using their own document layouts for important documents, such as invoices. 

Fortunately, in today’s increasingly digital world, Pytesseract is not the only option for automated processing with OCR accuracy. There is a wide range of OCR technologies available — some template-based and some cognitive — so you will have to think about what type of OCR engine you are looking for when it comes to making your accounts payable (or any other form of data processing) system more efficient.

If you are looking for the most effective solution, you will likely want to consider cognitive OCR rather than template-based OCR. This is because cognitive OCR software will require less maintenance over time and it can help you to automate much more of your process than a template-based solution will be able to.

Cognitive OCR technology utilizes artificial intelligence to approximate the same processing methods that the human brain uses so that it can more accurately extract information from documents. 

Rossum’s AI Engine learns to recognize the information in your accounts payable and receivable documents and makes generalized decisions based on the thousands of pieces of data it has already processed. This lets it focus on the information within the document rather than getting confused because the layout, language, or type of data is new to it. 

How to calculate OCR accuracy

Adopting a new system can be incredibly daunting — especially if you are unsure of how the system will work for your business and what exactly it will improve. This may be one of the reasons why close to 90% of today’s invoices are still being processed manually. 

But, what is the purpose of advancing technology if we are not going to use it? Especially when using it can dramatically speed up and reduce the costs of your accounts payable system. But, of course, simply adopting and implementing a new solution is not enough to know how well it is benefiting your business.

This is why it is incredibly important to track progress and measure accuracy over time. When it comes to finding your OCR accuracy rate it can be helpful to have an analysis or reporting system that helps you to track your data and highlight progress as well as the areas that need improvement. 

Rossum uses a Usage Reporting Dashboard to do just that. The Rossum dashboard provides you with filters that you can use to isolate specific date ranges, users, or queues.

Additionally, you can see graphs that map usage, turnaround time, the number of corrections made, time per document, automation, and more. Data analysis tools are a great way to better understand just how much a new system is helping your business become more efficient.

The accuracy of your OCR system is going to depend on a number of factors, the first of which is which type of OCR technology you are using. While a template-based OCR system can be a step above manual data capturing methods, it will still require a higher level of manual correction and rule creation than cognitive OCR. 

Once you’ve made corrections and created new templates, the accuracy of a template-based OCR solution will improve, but it will initially require a lot of time and effort to effectively process your incoming invoices.

Compared to template-based OCR, a cognitive OCR solution like Rossum is ready to go right out of the box — no constant setup required. With a user-friendly interface, Rossum can operate 6-8x faster than manual data entry methods. In other words, cognitive OCR can dramatically improve your accuracy while also decreasing the time it takes to process your documents.

How to improve OCR accuracy

The first step in improving your OCR accuracy is knowing how to measure OCR accuracy. In reality, an OCR accuracy measurement is not the most straightforward measurement to take. OCR software actually offers a confidence level (0-9) for all of the characters that it detects but whether or not that character is truly accurate can only be determined by a human being. 

Unfortunately, taking the time to proofread or manually re-enter all of the data that your OCR technology has collected is a time-consuming and draining process. When it comes to how to best improve your OCR software’s accuracy, one study found that using a combination of methods was the most impactful way to do this. They also found that perhaps the most effective way to improve accuracy relies on humans manually correcting the mistakes made by the OCR technology.

Another solution that businesses can implement to improve the accuracy of their OCR technology is to use a more advanced artificial intelligence system rather than a template-based one. Today’s OCR solutions rely on machine learning and artificial intelligence rather than templates to identify the important information much like the human brain does.

Because machine learning solutions actually get more effective over time, they can become more accurate the more they are used. The longer you are using Rossum’s cognitive OCR, the less manual correction you will need to make over time and the more inherently accurate your data capture method will be.

OCR accuracy comparison

With so many different OCR software solutions available on the market, it can be difficult to decide which one may be the best fit for your business. Luckily, though, with all of the information available for free online, there is likely an OCR comparison chart that details the pros and cons of many of the most popular OCR software options available today.

If you have certain systems in mind — for example, Tesseract, RossumI, or Azure — you could do a simple web search for “azure OCR accuracy,” “how accurate is Rossum.ai,” or “tesseract OCR accuracy percentage.” These searches can help you to determine a baseline of how useful a system may be for your business.

However, if you are looking for a solution that can really help dramatically improve the efficiency and accuracy of your document processing system, you may want to focus your search on a cognitive OCR solution, like Rossum. 

A system like Rossum’s can be used right out of the box and can reach 95% accuracy within 30 days on its own so that your accounts payable (or any other data-heavy) team can focus their efforts on other more value-added tasks. 

Rossum’s AI-driven OCR software can help you automate up to 98% of your processes, whereas traditional template-based OCR software could only automate up to 50% of your overall workload. With efficiency, user-friendliness, and OCR accuracy, AI-driven document processing is hard to beat.