How does AI OCR work?

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Data capture solutions: Traditional OCR vs. Cognitive

It is simple, the future lies in AI-powered data capture. It is possible to leave behind the issues of manual and template-based data capture and achieve a solution with high accuracy, speed, and that is cost-efficient. Read this post to learn how.

What is OCR software?

What is OCR software? Optical character recognition (OCR) software can refer to any computer program or application that converts images into machine-encoded text. These images could include typed, handwritten, or printed text. Furthermore, these images often consist of scans of important business documents such as invoices, purchase orders, packing lists, insurance claims, and more. 

The main function of OCR software is to take the unstructured data in these images, capture it, and convert it into structured data that can be understood and used by machines. As a more efficient alternative to manual data entry, OCR image to text software is widely used as a key element in the document management workflows of various teams and organizations throughout the world. 

What is OCR? Optical character recognition technology saves businesses time and money by providing automation and data capture to document-based processes. This technology scans images of documents and extracts the data within them. There are two general types of OCR – template-based and AI-enabled, or cognitive. Which type of OCR to use will depend on a number of factors. 

Above all, accuracy is key in automated data capture. If the OCR software you deploy still requires human data professionals to constantly correct its mistakes, you won’t see much of an ROI. In various industries, OCR brings efficiency and automation. The OCR meaning in accounting refers to the ability of OCR to scan transactional documents like receipts and automatically flag fraudulent transactions. The OCR meaning in insurance refers to the ability of OCR to make it easier to process insurance claims by automating the data capture process. 

The best online OCR technology does more than just capture data. Most organizations do not have a single platform that they can use to manage their documents. Consequently, document processing is often done in a fairly haphazard manner, with different teams processing the data they need for their processes. 

In order to prevent any documents from getting lost or miscategorized, a better solution is needed. A comprehensive platform like Rossum enables you to take control of your entire document processing workflow. From data capture all the way to post-processing, Rossum makes it easy to analyze, capture data, and route documents to their appropriate destination.

How does OCR work?

How does OCR work? Traditional, template-based OCR was a real breakthrough in automated data capture technology. To this day, it remains one of the most popular methods for extracting data in business, despite its flaws. How this kind of system usually works is to scan the document and extract all text within the document. Then, the text is identified and associated with the appropriate data fields using templates or text-based rules. These templates help the system to know where to look for data and what information is stored in the document. Overall, traditional OCR works tolerably well in situations where there is very little variability.

For example, many IRS documents are completely consistent in format and style, meaning that the IRS can easily use a system like this to get accurate, fast data capture. However, in most of the business world, important documents actually have a lot of variabilities. You may think all invoices look the same, but the placement, fonts, and even the colors are most likely different across every single different vendor you work with. 

The changes in layout can introduce challenges to template-based OCR and can result in inaccurate data capture. You could create a new template for every single vendor, but each template takes hours to build. If you have hundreds of vendors, this is not practical. 

Let’s take a look at how OCR works in Python. Python is an easy programming language to learn and has been used by many to build their own in-house AI-enabled OCR solutions. Most often, a Python program like this relies on the Tesseract OCR library, an open-source OCR engine sponsored by Google. To build a simple OCR program, only a few lines of code are needed. However, the program will have no interface, and it would not be practical to use in a business setting. A good deal more effort and research is required to build a business-ready, cognitive OCR solution. 

Fortunately, there is another newer kind of OCR called cognitive OCR. Cognitive OCR from Rossum uses AI technology to “read” documents more like a human would. First, the system skims through the document and builds a spatial map of where all the text fields are. Then, it identifies what document this is and what data is located within the text fields using its machine learning features. Finally, it uses its optical character recognition scanner to go character by character and carefully extract the data. This overall system achieves high levels of accuracy and can be used on hundreds of documents to instantly capture every single line item of data. 

Why do we need OCR?

Why do we need OCR? There are many reasons why OCR is needed in the world today. Right now, thousands of lifetimes are spent every year just doing the soul-crushing work of managing documents and extracting data. Employees are becoming demotivated, and valuable team members are being lost. OCR can end this chaos today and enable you and your team to conquer the endless challenges of document management. Plus, the time saved in various departments and teams allows those employees to focus on higher-value, more strategic activities that can actually grow your business. 

OCR accounting software can be used to automate the accounts payable process by scanning and extracting data from invoices. It can also be used to monitor transactions and financial statements, as we’ve already mentioned. In healthcare, OCR streamlines the mountains of paperwork that wastes so much time and so many resources. Insurance companies also have to deal with several different kinds of documents, from policies to claims and certificates of liability. An OCR solution can easily handle all these documents. Operations departments are another place where OCR can bring many benefits for its ability to alleviate your employees from the burden of processing business documents. Rossum’s AI-enabled OCR solution comes pre-trained, out-of-the-box to manage all of the different kinds of documents mentioned here and many more. To discover all the different ways Rossum can be useful for you, here are the other use cases

OCR algorithm

There are many different kinds of OCR algorithms. An OCR algorithm is the strategy that the OCR code uses to capture the data. Generally, they operate on a character-by-character basis. The system scans an area where a character is present and then deduces what that character is by isolating it from the background. 

Every pixel where the background color is present receives a value of zero, and everywhere some pixel of the character is present receives a 1. Basically, this creates a map of 1s and 0s which the system can compare to other maps that it has recorded to identify the corresponding character. It applies this process to every character and groups these letters into words based on rules that define letter-spacing. 

Different algorithms have different pros and cons. In light of the various disadvantages of template-based OCR, the best algorithm for OCR is going to be AI-enabled. One AI-enabled OCR software is the Tesseract OCR algorithm that we have already mentioned. Accuracy across many variations is crucial when it comes to business data capture. 

Unfortunately, Tesseract OCR still has limited functionality when it comes to document variations, such as fonts, formats, and colors. In the business world, fast implementation and rapid results are mandatory features. Rossum provides this because its optical character recognition algorithm comes pre-trained to manage thousands of different kinds of documents. To learn more about how Rossum AI works, feel free to read our optical character recognition documentation

Optical Character Recognition example

One great optical character recognition example is how Rossum’s OCR engine can streamline invoice management. For many years, employees in the accounts payable department have had to deal with hundreds of invoices, manually processing them and sending the data to the accounting team. 

With Rossum, all that changes. Rossum enables you to completely automate your invoice workflow using AI to extract the data and send it to the proper destination for approval before sending it to your accounting department to be paid. Because Rossum is a cloud-based technology, it can also give you 24/7 visibility into the document management process. The optical character recognition machine learning technology gives Rossum the ability to process one, or hundreds of invoices, freeing up your AP team to focus on the activities that matter most to your business. This is a great example of the OCR meaning in business. 

Best OCR software

Finding the best OCR software can be a challenge. The best OCR scanner app is one that is easy to use and effective, but also highly accurate. One great way to determine whether or not the OCR software you are looking at possesses these features is to read the reviews from sources like G2, Google, or Capterra. 

Depending on your needs, you might want to understand what would be the best OCR software for Mac vs. a PC and/or the best OCR software for handwriting recognition. Reading these reviews can help you find the best optical character recognition software to fit your needs. At the end of the day, you may require something more comprehensive. An intelligent document processing (IDP) platform like Rossum AI may be the best choice for its flexibility, ease of use, and effective results.

Say hello to the future of OCR - meet Rossum

Capture data from structured & unstructured documents with the world's easiest and most accurate data capture system powered by AI.