OCR and AI: How Modern Automated Processing Works

OCR and AI: How Modern Automated Processing Works

AI is changing the game for OCR solutions. Here’s how modern automated processing works.


When Optical Character Recognition (OCR) was introduced in the 1980s and 1990s to convert printed or handwritten text into a machine-readable format, there were promises of significant time and cost savings. Unfortunately, traditional OCR technologies never quite hit the mark. 

These solutions were generally faster than manual data entry, but they still required significant manual work and oversight. After all, using a traditional OCR system means spending time creating templates, setting up rules, and manually reviewing data.

Luckily, modern OCR systems are far more accurate and automated than the first iterations of OCR — and even solutions introduced just a few years ago! These artificial intelligence-backed OCR systems are finally fulfilling the time and cost savings promised decades ago, capturing data from documents quickly and accurately. 

Modern AI-powered OCR not only recognizes contextual clues within documents to correctly identify everything from addresses to proper names to sum totals but can then utilize that information to make data-driven decisions. As a result, there’s little need for manual exceptions and pre-sorting or template-based document preparation before the automated process begins.

How OCR Works in the Modern World

How exactly does modern OCR technology work? Modern OCR solutions use artificial intelligence and algorithms to form a neural network. The result is hyper-accurate, automated processing capable of understanding context, skim reading, and making accurate, data-based decisions. More specifically, OCR can be broken down into a few key stages:

First is the pre-processing and analysis stage. This involves importing the document and is about standardizing inputs and ensuring alignment and proper sizing to decrease variables. It can also involve object detection, which will help the software focus on specific areas later on, and the elimination of imperfections, such as stains, dust particles, and hairs, to create a more refined image.

Next comes binary conversion, a process that makes recognizing characters easier for OCR systems. Here, the refined image document is converted into a bi-level image containing only black and white colors. The system will read the white areas as background and the dark areas as characters to be processed.

After the OCR solution has differentiated between the background and characters, it processes the black areas to identify letters and digits. Intelligent character recognition generally occurs one character or text block at a time and uses one of two algorithm types: pattern recognition or feature detection.

OCR systems that use a pattern recognition algorithm insert text in various formats and fonts into the software. This then serves as a basis for comparison that enables the OCR solution to recognize characters after comparing the pixels of the scanned letters with known fonts.

On the other hand, systems that use a feature detection algorithm are a little more sophisticated. They divide characters into their components and identify them by applying rules based on numbers’ and letters’ unique features, such as curves, corners, and crossed or angled lines. 

For example, an OCR system that relies on a feature detection algorithm might read two perpendicular lines, the vertical line ending at the horizontal one’s midpoint. It might then compare those physical features to find that the character corresponds to the letter T.

Next is the contextual formatting identification phase. It’s possible to train OCR solutions to identify specific patterns and process them accordingly. For example, an OCR system might automatically realize that a document contains sensitive patient information that requires higher-level human review and send it to the doctor’s staff instead of the general bookkeeping staff.

Comparing Traditional OCR to Modern, AI-Supported Solutions

Traditional OCR solutions are certainly a step up from manual data entry. After all, manually retyping data is a slow, unscalable, and expensive process that leaves plenty of room for human error and employee dissatisfaction. However, traditional OCR systems aren’t as automated or accurate as we might like them to be. Each new format means new rules and templates, which becomes frustrating and time-consuming. 

Ultimately, traditional, legacy OCR solutions cannot significantly cut time and budget for organizations with high volumes of documents and multiple vendors due to their template-based nature and the manual work required to obtain accurate results.

It’s a different story with AI-powered OCR systems like Rossum. Compared to traditional OCR solutions, these modern systems can read documents the way a human would and offer plenty of benefits. Advanced solutions are not only faster and more accurate, but they also free up resources, ensure a fast ROI, boost employee productivity, and cut costs. 

While it costs an average of $13 to manually process an invoice and $4 to process one with a template-based OCR, it costs less than $1 with an AI OCR. Sometimes, it can cost as low as $0.05 per invoice!

What’s more, they can process electronic, typed, and hand-written documents in any format and are constantly learning and improving, so you can count on an AI-powered OCR system to quickly give you accurate information, whether you have a few vendors that use the same format or hundreds of unique formats. 

Plus, processing can be six times faster than manual processing, up to 98% accurate, and reduce keystrokes by 97%, saving organizations time and energy.

Applications of AI-Driven OCR Technology

Countless organizations use modern OCR technology to streamline their document processing, cut costs, improve accessibility, save time, and keep their employees from burnout after dedicating their time to tedious, manual data extraction.

For example, the following industries use AI-based OCR technology to great effect. 

  • Banking 
    • Digitize older bank statements, checks, and records
    • Optimize security
    • Improve data management
    • Enhance customer experiences
  • Hospitals
    • Optimize scanning, searching, and storing patient records and insurance payments
    • Faster sorting of letters and packages
  • Airports
    • Automatic data extraction from passports
    • Maximize revenue from parking lots and find stolen cars by tracking license plates

However, OCR shines brightest when it comes to automating accounts payable processes. After all, more than 550 billion invoices are expected to be issued annually — and invoices generally vary by issuer. 

AI-driven OCR software makes scanning and extracting accurate data from invoices simple. Instead of spending hours upon hours manually entering data or configuring new templates for every alteration, you can sit back and let your advanced OCR solution handle it.

Check out some of Rossum’s customer stories to see some more examples of how companies are using AI-driven OCR to increase efficiency, boost revenue and productivity, and reduce risk.

Save Time and Cut Costs With Rossum

The OCR solutions of old required a new template for every document format and were prone to inaccuracies, but Rossum is different. Our platform is reliable and can achieve pinpoint accuracy thanks to our highly adaptable AI neural engine. Not only does it understand terminology, layouts, and formatting cues, but it can also quickly adapt to changes.

With Rossum, you can save time, money, and resources, giving you time to focus on more profitable and personally fulfilling tasks.
Contact us today to receive a demonstration and learn about the difference between Rossum and other IDP solutions for yourself!

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