Is traditional Optical Character Recognition (OCR) failing to meet your needs?
Tired of manual data entry and its endless potential for human error?
It's time to step into the future of document processing. AI-powered OCR is a game-changer, combining the power of artificial intelligence with traditional OCR to create an intelligent, adaptable, and highly accurate solution.
Our OCR AI solution, in particular, is revolutionizing how people handle any document, from handwritten medical forms to historical archives. Save time, reduce costs, and unlock new insights from your most important documents.
Read on to discover how AI OCR works, its incredible applications, and why it's the smarter way to manage your documents.
Discover:
- What is AI-powered OCR?
- Benefits of AI-powered OCR solutions
- How does AI OCR work?
- AI OCR case study
- Drawbacks, concerns, and limits of AI OCR
- Use cases for AI OCR
What is AI-Powered OCR?
AI OCR (Optical Character Recognition) is an advanced technology that uses artificial intelligence and machine learning to recognize and extract data from various types of documents and images.
Unlike traditional OCR, AI OCR employs neural networks to understand text from image-based documents (handwritten or printed text), converting images into machine-encoded text. This data is then editable or searchable.
This conversion is highly accurate across different fonts, layouts, low-quality images, PDFs, or photos. By automating the data extraction from a wide range of documents, AI OCR significantly eliminates the need for manual data entry, improving efficiency and accuracy.
In Grooper, we utilize various OCR methods, combined with AI, to convert documents into digital formats with superior data recognition accuracy.
How Does AI OCR Work?
It should be noted that if a document does have native digital text, OCR isn’t always necessary. However, with OCR alone, problems often arise with:
- Semi-illegible handwritten text
- Weird fonts
- Smudges on the document that obscure parts of a word
If you have those, there’s a good chance you’ll get some bad OCR. This is where the combination of AI and OCR comes in.
OCR and AI Work Together Like a Writer and an Editor
Thanks to the predictive nature of AI, it can take the parts that standard OCR struggled with and, through its model (assuming this is an LLM-based model), analyze sections of the document. The AI will recognize patterns or features within the text that it will then use to identify not just words, but individual characters within words.
This works by OCR running first. After the text has been recognized, AI refines it, fixing errors and making it more readable.
Think of this like a writer and editor. OCR ‘writes’ the machine-readable text, and the AI comes after and edits the text.
Use Cases for AI OCR
AI OCR is a way for professionals to improve efficiency and accuracy in their everyday work operations. By leveraging AI and machine learning, AI OCR goes beyond traditional text recognition to intelligently extract text and data from documents of varying document layouts and document scan qualities.
Preparation for AI-based Systems
The biggest use case right now for OCR is the front-end of AI-powered processes. AI is still largely powered by text (let's ignore multi-modal AI for the moment).
In order to get the best out of current AI systems, you need the cleanest text you can get from your documents. That's what AI-based OCR gives you. Large Language Models (LLMs) are the current darling of AI - and they are amazing. Grooper users are achieving significantly better results by combining Grooper's OCR with an LLM for data extraction.
There are use cases that can't be handled with OCR alone, such as those involving highly variable formats and data variations. LLMs can effectively manage these variations, provided they have clean text to serve as a basis for their work.
Healthcare Documents
One of AI and OCR's most impactful uses is in healthcare, where it can convert healthcare-related records into digital data, such as:
- Patient records
- Forms with machine print and/or handwriting
- Lab results
- Insurance claim documents supporting RCM
The benefits include reduced manual data entry (which is prone to errors), significant time savings, and improved patient care through timely access to critical information.
Get Our Case Study on AI and OCR
Here's a great example of how a leading manufacturer of healthcare devices is using AI and OCR to improve patient care. Learn how they are using Microsoft Azure and other technologies to save significant time and accelerate workflows. You will discover:
- How they overcame "garbage recognition" from competitor OCR software
- The automation they are using to rapidly identify and organize 30 different document types
GET THE CASE STUDY:
Banking and Financial Documents
At Grooper, many credit unions and banks across the United States use our AI OCR software to improve their operations and provide faster customer service.
How?
For starters, they are using AI OCR to save significant time by processing loan applications, invoices, bank statements, checks, and other financial documents with high accuracy. It helps identify and verify information for KYC (Know Your Customer) processes, detect fraud patterns, and automate accounts payable, speeding up processes and enhancing security.
Check out our case studies to learn much more about how banking and financial services are using Grooper's AI OCR software. Here's a great example of banking financial document automation with AI and OCR.
Historical Documents - Preservation and Enhancement
Not to sound like a broken record, but we at Grooper are the experts at this. We've been using OCR and AI for over 10 years to preserve important historical records across the US.
AI OCR plays a vital role in preserving historical and cultural heritage by digitizing vast amounts of archives. This includes 100-year-old leather-bound books filled with marriage, divorce, and death records, to large format blueprints.
Solutions like Grooper make these records easier than ever for the public (or researchers) to access and search. A great example is how Grooper AI digitized over 150 years of historic records for Sedgwick County, Kansas, the county that includes the city of Wichita. Our AI software saved thousands of records that were stored in terrible conditions and were quickly deteriorating.
AI OCR solutions also enable image tagging, allowing systems to label and identify general objects, landmarks, locations, and even handwriting within images. This is important for geospatial intelligence and digital asset management.
Improving Customer Service
AI OCR improves customer service by automating the processing of physical documents like any handwritten document, contracts, complaint letters, or feedback forms. For example, one of our clients (a large insurance company) uses our AI software to automate the process of manual data entry from handwritten contracts.
This OCR AI solution is saving them about 20 minutes per contract, translating to thousands of hours a year. Their customers are then able to spend their time with their customers, increasing customer satisfaction and happiness.
OCR AI handwriting recognition solutions enable businesses to quickly analyze huge amounts of documents, reducing response times and improving customer satisfaction by digitizing and indexing information for efficient customer retrieval. This technology fundamentally changes how organizations manage and utilize their document repositories.
Benefits of AI-Powered OCR Solutions
AI OCR, the powerful combination of AI and OCR, is transforming how businesses manage their documents and data. It's more than just digitizing text; it's about intelligent data extraction and process automation that delivers actual, tangible benefits for people across many industries.
Enhanced Accuracy and Reliability
Traditional OCR struggles greatly with handwriting or complex documents. But one of the most significant advantages of AI-powered OCR is its high accuracy, especially when recognizing handwriting, varying layouts, diverse fonts, or even bad scans.
That's because AI algorithms (especially those leveraging deep learning) can analyze diverse contexts and learn from new data (with human supervision). This continuous learning and improving capability means fewer OCR errors, drastically reducing the need for manual review and correction of extracted data.
Adding AI to OCR is even more important when processing complex documents, or those with handwriting or multiple fonts. AI optical character recognition can also detect anomalies within documents that can help identify financial discrepancies or flag missing signatures.
Increased Efficiency and Productivity
AI OCR dramatically boosts work efficiency by automating what was once a time-consuming and error-prone task: manual data entry.
Businesses can process vast volumes of very common documents — like invoices and receipts, to contracts and patient records — at speeds impossible for human teams.
This automation frees up employees from tough, repetitive, low-value work that people usually don't like performing anyway.
Instead, employees can focus on higher-value tasks like customer service, which helps the company and overall productivity.
Significant Cost Savings
The reduction in manual data entry labor translates directly into noticeable cost savings. In addition, the use of AI OCR reduces the costs of document storage. Think about it: storing and managing digital documents is a lot cheaper than having to house box upon box of physical documents.
By nearly eliminating manual errors, OCR AI solutions save costs for correcting mistakes, reprocessing documents, and potential compliance penalties. This efficiency gain provides a clear return on investment for businesses implementing AI OCR solutions.
Adaptability and Versatility
The adaptability of AI-powered OCR represents a significant improvement over legacy OCR, which struggles with reading handwriting or documents that use multiple fonts, such as checks.
OCR that leverages AI is not limited to just recognizing text. With the broader capabilities of AI, OCR can process and understand data from images, and even analyze elements within different document structures.
This versatility makes AI-powered OCR valuable to many departments and industries, as it's able to process virtually any document, such as:
- Student transcripts
- Invoices that span multiple pages or have many line items
- Checks with signatures
- Legal documents like leases or contracts
- Driver's licenses
- 100-year-old historical documents
Many OCR AI systems also seamlessly integrate with downstream business data systems, such as ECMs, accounting software, or student information systems. This means existing workflows are simplified and easier.
By providing high accuracy, driving increased efficiency and productivity, delivering considerable cost savings, and offering unmatched adaptability and versatility, AI-powered OCR empowers professionals to work smarter, faster, and with greater confidence in their data.
Drawbacks, Concerns, and Limitations of AI OCR
Recently, Matt (one of our top developers at Grooper) saw some of our AI OCR software's newest capabilities and how easy it makes processing documents.
He said, "Well, I guess I'm obsolete now."
Matt was joking, but there's some truth in every joke. He has almost unparalleled knowledge of how to use our software. But AI tools are truly democratizing what was once challenging work that only highly skilled, experienced people could do.
Tasks like creating data models to capture data from unique documents used to take weeks or months. With AI assistance, many more people create the same models in a couple of hours.
For example, we have a client that dramatically accelerated OCR processing. To accomplish a certain project:
- Previously required two people working solid for three months.
- Now only needs one person working for two weeks with 10-15% better accuracy as a result
The point is that as AI advances, there are fewer challenges or limits to OCR powered by AI. But here are the current challenges of using AI OCR:
Messy Data
AI-based OCR solutions may seem perfect (and they are improving rapidly), but they are far from it. As with any AI, it is susceptible to ingesting poor or incorrect data, which will affect its accuracy.
For traditional OCR, scanning an image with poor lighting can lead to errors in recognition, as well as in refining and processing. In this example, traditional OCR performed poorly in accurately recognizing handwriting. But once AI is added to OCR, accuracy is drastically improved.
Of course, gathering high-quality, accurate data that AI can use can be time-consuming as well. So, this is something companies will have to weigh before choosing to rely heavily on AI OCR.
Feeding AI OCR an image of poor quality can still result in errors. Yes, we did tout the AI’s ability to correct and refine OCR errors, but again, this is a machine, and the general rule still applies: garbage in, garbage out.
It is not psychic. It is not perfect.
Language Limitations
If your AI hasn’t yet been trained on varying language types, then AI OCR may struggle with non-Latin-based languages
Data Privacy
There is a ton of raw power with a technology like AI. That leads to hype and misunderstandings. Then add an OCR component to capture your data, and people will have even stronger concerns.
With Grooper, you have control of your own data. We can't see your data. And, unless you tell us, we have no idea what kinds of documents you are processing, or what sensitive information you are handling.
And Grooper integrates with AI directly through APIs. This means we have enterprise-level security built into our integrations.
You get an API key and a secure endpoint. This is the same level of security and encryption that your online banking uses when you log into your bank's website.
Complexity
Many AI systems require specialized expertise for development, maintenance, and possibly all aspects of usage.
Tasks like managing data models, working within interfaces that are not user-friendly, and integrating with other data systems can be very complex. In the past, it might even have required a team of developers to implement and maintain the system.
But with the AI advances of the last year or two, these hurdles are smaller than ever. That is certainly true of Grooper AI OCR software.
By giving simple instructions to AI in Grooper, people with no IT or coding experience can create a data model, which is like a blueprint for extracting data from a document. Many times, a lone IT employee can handle all the implementation and maintenance.
Contextual Limitations
This was once a real problem for traditional OCR, as it didn't understand the information on the page, nor what the data meant.
But with today's AI OCR, all of that has changed. You can ask virtually any question about your documents and data.
The answers you receive can save you valuable time and costs, and help you beat deadlines. Searching for a particular purchase order or conducting legal discovery no longer requires countless hours of searching.
