What Affects Driver's License OCR Accuracy: Key Challenges & Solutions

Simply put, OCR technology converts printed or handwritten text into structured, machine-readable data, allowing IDV software to extract text from scanned identity documents. This data is then used for automated authenticity checks and may also be reviewed by human agents.  

OCR verification typically takes just seconds within the broader ID authentication process, but its function is critical. Unlike unique identification numbers or machine-readable zone (MRZ) codes with check digits, a scanned ID image lacks built-in validation checks. A robust IDV solution must accurately extract and interpret this data to ensure reliable verification. 

What’s more, in remote scenarios, OCR technology replaces a human inspector, who would otherwise examine the whole document while also focusing on details. This makes OCR a crucial component in ensuring both accuracy and security in automated ID verification.

Technically, OCR works the same way for any image containing text. The process consists of three main stages:

Let’s take a look at a real-world example for clarity.

When a user scans their driver’s license, how does the system distinguish, for example, a Spanish license from 2013 from one issued in 2004?

A neural network trained on a vast dataset of ID templates governs the process. It first detects the document type by applying the most suitable driver’s license template to the scanned document. In this example, it chooses from Spanish driver’s licenses. Although the layouts are quite similar, the neural network can spot even small differences.

Next, a text detection algorithm locates the text areas in the image and extracts only the data needed for further processing—such as the holder’s personal details and key document information, including the issuing state and date of issue. By the way, driver’s license OCR also supports processing machine-readable data like the MRZ code. 

But what about the final stage? When scanning with a mobile phone, misreadings can occur due to blurriness, glare, or low image quality. Common OCR errors include mistaking the letter “O” for the digit “0”, or “I” for “1.” These can be corrected during post-processing. OCR technology can automatically resolve character recognition inconsistencies by factoring in language specifics and document types. 

Once all the text from the visual inspection zone and MRZ is extracted, structured, and verified—the core function of driver’s license OCR—the system proceeds with authenticity checks. Importantly, the data from the visual inspection zone and MRZ read via OCR is supplemented with additional data captured by other technologies within the IDV software, such as barcode information and data from the RFID chip. 

Some dynamic security features, such as Multiple Laser Images (MLIs), contain textual data. For instance, the MLI on the front of an Arizona driver’s license includes the holder’s date of birth. The OCR algorithm captures and processes this text, allowing the system to cross-check it during further document authenticity checks.

Driver's license OCR can recognize text embedded in UV security features when scanning a document with an ID reader equipped with multiple light sources. 

In some countries, such as the USA, personal details like the holder’s date of birth or first and last names may be printed in UV ink. This hidden data can then be cross-checked against the visible printed information for verification.

OCR technology may seem straightforward—you provide an image of a document, and the algorithm recognizes and converts the text into structured data. However, in practice, errors can occur at every stage of the process.  

The following pitfalls primarily affect remote driver’s license verification, but they can also arise during on-site checks.

Many countries still issue paper-based driver’s licenses protected by a laminate layer. Examples are Chile’s 2003 series and the Chinese driver’s license from 2012.

Scanning laminated IDs can be challenging, especially for mobile users. The reflective surface often causes glare, particularly when using a flash, leading to overexposed areas where OCR can’t accurately recognize text. This may require a retake, frustrating users who expect quick and seamless verification.

Driver’s licenses are domestic identity documents, meaning most countries issue them in their national language, which may be non-Latin. 

Some licenses are bilingual, featuring the official language alongside a globally recognized one. 

For example, a Cyprus driver's license presents the holder’s name in both Greek and English.

With such diversity, an IDV solution must be capable of recognizing these languages correctly. 

Driver’s licenses usually include three key dates: date of birth, issuance date, and expiration date—all critical for IDV. However, variations in calendar systems present an additional challenge. 

While most regions, including Europe and the Americas, use the Gregorian calendar (with slight formatting differences like DD/MM/YYYY and YYYY/MM/DD), some Asian countries follow unique chronology systems that appear in their identity documents. 

For example, Japan uses the Era calendar, which presents the date starting from the first year of the reigning Emperor’s rule. As a result, Japanese driver’s licenses often feature multiple eras within the same document, since birthdates and expiration dates may belong to different imperial eras.

A similar situation exists in Thailand, where the Thai Solar calendar is used, beginning from the year when Buddha entered nirvana. In this system, 2025 corresponds to 2568. Despite Thai driver’s licenses displaying both Thai and Gregorian dates, OCR technology must accurately interpret and convert these Thai calendar dates.

Driver’s licenses vary significantly in design and layout across countries and even states within the same country. Traditionally, most are landscape-oriented, but many portrait-oriented documents also exist.

Additionally, in federative countries such as Australia, Canada, the USA, and Mexico, driver’s licenses may differ in design and data fields across states.

With all this diversity, even the same Latin letter (for instance, “Q”) can appear very different across fonts and spacing styles. Some issuers also use mixed letter cases within a single document—for example, printing the holder’s last name in uppercase while the first name appears in sentence case. 

The front side of driver’s licenses can also vary, sometimes including an MRZ, which OCR must process. Interestingly, even licenses issued under regional standards, such as European driver’s licenses, can differ in this regard.

All these variations make driver’s license OCR more complex, requiring numerous templates that account for different font styles and layouts.

Holograms and intricate background patterns are essential security features that help prevent driver’s license counterfeiting. During on-site checks, these elements don’t interfere with verification, as document readers ensure high-quality ID scans. However, in remote driver’s license verification, bright and complex security features can cause issues, such as glare or overlapping text, making it harder for OCR algorithms to detect and recognize information accurately.

Since driver’s license OCR is the first stage of the IDV process, two key strategies can help ensure both user satisfaction and accuracy.

The first requirement for accurate OCR is obtaining high-quality scans of the front and back sides of the driver’s license. To achieve this, guiding users through the scanning process is essential. 

Providing step-by-step instructions can make scanning smoother and reduce errors. Small on-screen prompts, such as “Choose a well-lit room,” “Center your ID in the frame,” and “Flip the ID so it faces the camera,” can significantly reduce retakes caused by glare, tilt, or poor focus. Also, consider image preprocessing features, such as advanced document capture, to help users get the best scan on the first try.

Upgrading your IDV solution can also improve OCR accuracy. At the end of the day, you don’t need all the data displayed on a verified driver's license—you need pre-processed, well-structured information to complete the verification. 

Having an extensive template database ensures that OCR correctly identifies and structures data, preventing errors like placing an address in the name field. It also allows the system to recognize non-standard templates, which would be misread without a corresponding reference. 

A robust OCR system used by global companies should also support:

• Multiple languages, calendars, and data formats

• Lexical analysis to cross-check language, font, and formatting

• A comprehensive driver’s license template database

Regula Document Reader SDK is an advanced IDV solution with enhanced driver’s license OCR capabilities. It supports over 138 languages, including Chinese and Arabic, recognizes special characters, and extracts data from more than 600 field types, such as date of birth, ID number, and credit card numbers. Learn more about how it can streamline document verification in your environment.