How to Improve OCR Accuracy with AI
Is Your OCR Not Very Accurate?
Optical character recognition (OCR) is a technology used to convert scanned images or photographs of text into machine-readable text. It can be used as an AI accelerator to convert printed or handwritten text that can then be injected into business intelligence or content management platforms.
By having the data as electronic text, that information can be used for advanced analytics, near-elimination of manual data entry, and exceptional enterprise search capabilities, among many other benefits.
Why Imperfect OCR is a Problem
Getting anything less than 100% OCR accuracy creates massive error rates. Here’s why:
Say you’re getting 95% accuracy on an invoice and you need to extract 10 independent fields. The overall per-field accuracy is actually 60%, not 95% because .95 to the power of 10 is .60, or 60%.
Imagine – just 4% better OCR boosts accuracy to 90%. You may accept 95% accuracy for full-text search, but not for recognizing text and extracting it.
Automation needs 100% OCR accuracy, but it’s impossible using OCR alone.
7 Ways to Improve OCR Accuracy
Improving OCR accuracy involves optimizing both the source image and the OCR process itself. Here are 7 great ways you can improve your accuracy:
1. Use Azure OCR
1. Use Azure OCRAzure OCR is powered by AI and convolutional neural networks (CNNs). As a result, it excels at recognizing handwritten text and handling poor-quality images.
Paired with Grooper, Azure is far better than traditional OCR engines. Grooper improves Azure OCR by combining its results with those from a traditional engine, making up for Azure’s limitations in precise character positioning and small numeric value recognition.
This “OCR synthesis” approach ensures comprehensive and accurate data capture.
2. OCR Synthesis
Some OCR software (like Grooper) can use an OCR Synthesis suite of operations to further refine OCR results. They do this through:
- Font Pitch Detection, which corrects spacing issues caused by variable-width fonts
- Image Segmentation performs OCR on distinct regions like table cells, improving accuracy within complex layouts
- Iterative Processing performs multiple OCR passes, capturing previously missed characters
- Segment Reprocessing re-analyzes poorly recognized text segments
3. Use High-Quality Source Images:
3. - Resolution (DPI): Scan at a minimum of 300 dpi, ideally 600+ dpi for finer details.
- Increase Image Contrast: Ensure even lighting, avoiding shadows, glare, and low contrast. Increase contrast between text and background for better clarity.
- Cleanliness: Use clean, undamaged, wrinkle-free originals. Remove any noise, specks, or artifacts from the image that could be misinterpreted as text.
- Straight Scans: Ensure the document is scanned straight (deskewing). Skewed images distort characters and reduce accuracy.
4. Image Pre-processing:
4. Image Pre-processing:Image processing profiles in Grooper allow for both temporary or permanent image enhancements. Temporary adjustments, applied during the recognition process, improve OCR accuracy without altering the original document image.
- Resizing: Resize images appropriately. A common recommendation is resizing to about 1/10 of the original size (1.5mm x 1mm or less).
- Binarization: Convert images to black and white. This simplifies text recognition by creating a clear distinction between text and background.
5. Noise Reduction
5. Noise ReductionApply noise reduction or smoothing techniques to remove any remaining digital artifacts in the foreground or background.
These adjustments can include contrast enhancement and line removal, optimizing images specifically for OCR. By combining these technologies, you can achieve significantly higher OCR accuracy, even with challenging documents.
6. OCR Software and Settings:
6. OCR Software and Settings:Choose the Right Software: Select OCR software with accuracy-optimized algorithms, including those using machine learning and neural networks. Different OCR engines use different algorithm/s, and each has its own strengths and weaknesses.
Preprocessing Tools: Utilize built-in preprocessing tools like deskewing and binarization within the OCR software.
By addressing these points, you can significantly improve OCR accuracy and obtain more reliable results.
Free Cheat Sheet: How to Select the Most Accurate OCR Software
Is your OCR not very accurate?
There are many things that make some OCR software much more accurate (and help you save more time and money) than others.
In this free Cheat Sheet, you will discover the most important qualities that make for the most accurate OCR software. You will learn what to look for in an OCR software, including:
- What modern OCR method is a big improvement over traditional OCR
- The 3 key AI technologies that do a lot of heavy lifting to make OCR much easier and accurate
- 16 Processing tools that take OCR from zero to hero
- 5 Breakthrough features of modern OCR
- How to improve OCR for handwriting
Download Now:
How Grooper Improves OCR Accuracy
Many attempts at improving OCR accuracy have been made over the years to improve work with documents. Even simple features like rubber band OCR and zonal OCR require accurate underlying character recognition.
Although there’s no such thing as 100% accurate OCR without human help, making a huge improvement is very possible.
How to Improve OCR Accuracy:
- Better scanner controls
- Improved quality of document images
- Use AI and multiple OCR engines
- Human-based design approach
All you need is 99% OCR accuracy to get 90% accurate character recognition. Intelligent document processing provides built-in data validations, fuzzy matching, lexicons, and human data review to make quick work of the outlying 10% needed for 100% accurate data extraction.
We’re asked all the time about testing our OCR and if our intelligent document processing is more accurate.
So we tested Grooper’s OCR as well as a few other text recognition applications…
And the OCR Accuracy Test Results Are…
Using the same documents in all three steps of the OCR application test, we quickly processed and validated the results using:
- OCR alone
- Grooper’s OCR Synthesis (multi-pass) + Standard OCR
- Grooper’s OCR Synthesis + Grooper’s Advanced Image Processing + Standard OCR
The OCR test results below prove the power of intelligent document processing as an AI accelerator. Feed your workflows, AI models like ChatGPT, and RPA tools with accurate and trustworthy data:
| Grooper’s OCR Synthesis, Grooper’s Image Processing and Standard OCR | Grooper’s OCR Synthesis and Standard OCR | OCR alone |
| 99.91 % | 78.09 % | 49.60 % |
All other document data capture solutions using OCR alone, or OCR with expensive third-party add-ons aren’t getting the job done if they only achieve somewhere between 49-78% accuracy. And even 95% accuracy limits the power of your automations.
You deserve accurate and dependable data, free of errors – and now it’s available, just the way you imagined it would be!
What’s the secret to better OCR accuracy? A whole lot of work (but not for you)! Tired of poor performance with everything we were using, we built Grooper from the ground up to meet the challenging demands of modern automation to be an AI accelerator
You get the benefit of our unique approach to intelligent document processing, based on 30 years of document data capture and patented technology with the United States Patent and Trademark Office.
Here are our two OCR patents:
- Patent 10,679,089 – Systems and Methods for Optical Character Recognition
- Patent 10,740,638 – Data Element Profiles and Overrides for Dynamic OCR-Based Data Extraction
Do you need accurate extraction from tough documents like bad scans, invoices with nested tables, or natural language documents like contracts? We’ll show you how Grooper works on these.
Grooper holds two patents from the U.S. Patent and Trademark Office
Enjoy the Freedom of Accurate OCR
You are free from relying on poor-performing, low accuracy document data capture solutions that recognize very little text.
With Grooper AI acceleration, you will discover new ways of working and uncover business-changing innovations. Now you only need limited human data review to process pages filled with complicated text.
Transform OCR workflows on your Windows machine with a uniquely powerful, proven, and patented technology.
Check out our OCR technology to learn more. Or learn how to speed up your OCR.
Imagine the cost savings and workflow improvements if you had 99% OCR accuracy. Hundreds of organizations have increased their customer service, drastically cut costs and innovated in new ways by improving OCR accuracy with Grooper.
Grooper has become the AI accelerator for many industry-first solutions in healthcare, financial services, oil and gas, education, and government.
Video: Improving OCR Accuracy with AI and Image Processing
See the difference that great image processing makes in OCR recognizing and extracting text much more accurately.
If your OCR is not very accurate, learn from an OCR industry expert. You will discover how to quickly improve data capture results in actual business documents.