How to Extract Information from IDs through OCR

If your customer onboarding process was a race against time, traditional ID verification would be like showing up on a bicycle while everyone else drives Formula 1 cars. 

Not exactly ideal when you’re trying to win customers.

(and keep your compliance team sane) 

When was the last time anyone said, ‘ Probably never, because that’s about as exciting as watching paint dry in slow motion.

The reality is, while most businesses are still playing the ID verification game like it’s 1999, some have figured out how to process documents at speed. And no, they didn’t hire an army of data entry specialists or sacrifice their firstborn to the compliance gods.

Let’s see how.

What is OCR ID Data Extraction? 

OCR ID data extraction turns physical identification documents into digital data that computers can analyze and process. It takes all that printed text from driver’s licenses, passports, and other ID documents and converts them into clean, structured data. 

In plain words, it reads text from IDs so systems can automatically process and store that information.

In even more plain words, OCR is a highly trained digital reader. 

Just as humans learn to recognize letters and numbers, OCR systems use artificial intelligence to identify and interpret text from images. But unlike humans who can get tired or distracted, OCR works like an ultra-fast, never-tired digital processor.

How to Extract Data from IDs Using OCR

OCR technology extracts ID information through a series of carefully designed steps. 

There are three stages:

• Pre-processing (Getting the right image and cleaning the moderate-quality images)
• Processing (Character recognition and interpretation)
• Post-processing (Recheck)

Here’s a detailed look at the process.

Step 1: Getting the Right Image (Pre-processing)

Quality image sets the foundation for reliable OCR processing. A clear, well-lit capture of an ID gives OCR systems their best chance at accurate readings. The system checks for proper resolution, lighting balance, and whether all document edges are visible. 

When something’s off – maybe the image is too dark or blurry – good OCR systems flag these issues right away.

To increase the chances of getting the right image, lay down clear guidelines about image requirements when collecting images and have specific filters programmed to not accept unprocessable images.

Step 2: Cleaning Up the Image (Pre-Processing)

Raw ID images often need some touch-ups before processing. This step handles those annoying reflections from glossy cards, straightens tilted images, and cleans up shadows. 

OCR systems often now have in-built capabilities for cleaning up images. Some technical processes that systems use for image cleaning include:

• Binarization: Converts the image to black and white using adaptive thresholding, making text stand out clearly against backgrounds
• Skew correction: Aligns tilted documents within 0.1-degree precision
  
• Noise reduction: Uses Gaussian filters to remove unwanted specks and patterns
• Geometric correction: Fixes document boundaries and perspective distortions
• Local contrast enhancement: Improves text visibility in poorly lit areas

Step 3: Character Recognition – Finding and Reading Text (Processing)

This is where artificial intelligence steps in. The system scans the cleaned image, spotting every bit of text – from the obvious name and number fields to smaller security text. 

Special attention goes to the MRZ (Machine Readable Zone) at the bottom of many IDs, which uses OCR-B font – a standardized typeface designed specifically for optical character recognition. 

Step 4: Making Sense of the Data (Processing)

Reading text is one thing – understanding it is another. OCR systems organize the extracted text into meaningful data fields. 

Names go with names, numbers with numbers, and dates with dates. The system also checks if everything makes sense – like making sure dates follow proper formats (DD/MM/YYYY, MM/DD/YYYY) and ID numbers match expected patterns.

Step 5: Double-Checking Everything (Post-Processing)

The final step runs multiple checks on the extracted data. 

Does the date format look right? Do the ID numbers follow the correct pattern? Modern OCR systems even cross-reference information between different parts of the ID (including machine readable zone), catching potential issues that human eyes might miss.

For higher security, it can detect and validate security features like UV patterns, holograms, and micro-text elements.

What Information Can OCR Extract from IDs?

Most organizations don’t need every single piece of information from an ID. 

But having a system that can accurately capture all these data points means flexibility in what gets used and stored.

Information that OCRs can extract from ID include:

1. Personal Identification Details – Names take top priority, and OCR handles full legal names, including special characters and different naming formats. Birth dates get captured and automatically formatted to standard data structures. Gender markers and nationality information round out the basic identity details.
2. Document-Specific Numbers – Every ID number matters. From passport numbers to driver’s license codes, OCR captures these unique identifiers. Security numbers, document versions, and even batch codes get logged and validated against expected formats.
3. Address Information – Street names, building numbers, postal codes, cities, and countries all get properly categorized. Smart OCR even handles different address formats across regions, making sense of various layouts and styles.
4. Expiry and Issue Information – Issue dates, expiration dates, and validity periods get extracted and converted to standardized formats. The system also catches renewal dates and validity zones where they exist.
5. Security Features – Modern OCR spots security elements like watermarks, holograms, and special printing patterns. While it might not validate them all, it helps flag their presence for additional verification steps.
6. Biometric Markers – Photo zones get identified and marked for further processing. Signature fields get isolated, and any biometric information codes present on the ID get captured.

What’s particularly useful is how OCR handles variations in layout. 

A US driver’s license and a European passport might show the same basic information but present it quite differently. Take dates, for example – while Americans write “04/22/2024”, Europeans note it as “22/04/2024”. Quality OCR understands these nuances and standardizes the output.

What happens if OCR makes a mistake?

When extracting information from IDs through OCR processing, three main types of errors can occur:

• Non-Word Errors – These happen when OCR misreads characters and creates nonsense text. Think “H3LLO” instead of “HELLO” or “8rown” instead of “Brown.” OCR systems with built-in dictionaries and pattern-matching capabilities can spot these. 
• Real-Word Errors – These are trickier because the mistake creates an actual word. When “SMITH” becomes “SMITE,” it might pass spell checks but fails context validation. Again, smart OCR systems can look at the full context of the field and expected patterns to spot these sneaky errors.

Long story short, the solution is to pick the right OCR system that has extensive capabilities. 

Alternatively, select a solution that lets you conduct secondary verification as well so you can spot errors by cross-checking.  

Benefits of Extracting ID Information With OCR

While manual ID processing creates bottlenecks in customer onboarding and increases operational costs, OCR fixes these fundamental issues. 

1. Processing Speed Increases

Manual ID verification takes minutes per document. OCR processes the same information in seconds. For organizations handling hundreds or thousands of IDs daily, these time savings add up quickly. Staff can focus on handling exceptions and providing better customer service instead of typing data.

2. Simplified Compliance

Financial regulations like KYC and AML require accurate record-keeping of ID verifications. OCR systems automatically create audit trails, store verification data securely, and maintain consistent processing standards. This makes compliance easier to manage and demonstrate.

3. Accuracy Improves

Human eyes get tired. Human fingers make typos. OCR maintains consistent accuracy levels throughout the day. Modern systems achieve accuracy rates above 99% on clear documents, significantly reducing data entry errors and the time spent fixing mistakes.

4. Cost Reduction

Every manual ID check costs money in terms of staff time and potential errors. OCR slashes these costs by automating the process. Organizations typically see significant savings in operational expenses, especially in high-volume verification environments.

5. Businesses Can Enhance Customer Experience

Nobody likes waiting while someone types their information into a system. OCR creates smooth, fast verification processes. Customers spend less time waiting, and organizations can process more verifications without adding staff.

6. Scalability Options

As verification volumes grow, manual processes require more staff. OCR systems scale up easily, handling increased volume without proportional cost increases. This makes growth planning simpler and more cost-effective.

Set Up OCR for ID Verification

Whether you’re a small business starting with ID verification or a large organization upgrading existing systems, the right approach makes all the difference in getting things running smoothly.

Think about it this way: just like you wouldn’t build a car from scratch when you need transportation, you don’t need to build an entire OCR system from the ground up. 

Modern API solutions provide all the sophisticated OCR capabilities while you maintain control over how you use them. Your team keeps focusing on what they do best – serving customers and growing the business.

Signzy’s OCR API, KYC suite, and DL check solutions offer this straightforward path to ID verification. Start processing IDs efficiently without getting caught up in technical complexities.