The Growing Reliance on AI-Powered OCR (1)
TABLE OF CONTENTS
What is OCR?
OCR is a technology that converts text on scanned or captured documents into machine-readable text. Once converted into digital text, computers can process the characters to edit, classify, analyze, store, or compare them against reliable database.
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In the context of ID Verification, OCR (Optical Character Recognition) is used to extract and digitize the text from identity documents, such as passports, driver’s licenses, or ID cards. This process involves scanning the ID, recognizing the characters and other relevant data (such as the name, date of birth, and document number), and converting them into machine-readable text. OCR technology helps automate the verification process by quickly extracting and verifying the information on an ID to ensure its authenticity and match the user's provided details in real-time. This speeds up the verification process and reduces the risk of human error.
The Shift Towards AI-Powered OCR
The transition from traditional OCR to AI-powered OCR is driven by multiple factors that address long-standing limitations in document recognition. One major challenge is the way users submit their documents—often in poor conditions, with skewed angles, glare, and low resolution. Another challenge is that traditional OCR struggles with modern, real-world variations in fonts, text textures, and layouts mainly due to the fact that a single government issues multiple ID Documents, that don’t necessairly have a single standardized format. AI-powered OCR is solving these issues by leveraging advanced image processing and deep learning models.
What Drives the Adoption of AI-enhanced OCR in the ID Verification Sector
1. Users Submit ID Documents Incorrectly (Skewed, Angled, or Poorly Lit)
One of the biggest challenges in ID verification is human error during document capture. Many users take photos of their ID cards, passports, or driver’s licenses at improper angles, leading to skewed text, partial obstructions, glare from plastic covers, and poor focus.
2. Traditional OCR is not powerful when faced with multiple scripts, unusual fonts, text spacing rules, and layouts.
Unlike AI-powered OCR can handle a wider variety of text formats, traditional OCR relies on rigid templates and predefined character recognition patterns, which limit its effectiveness when faced with unusual fonts, poor-quality images.
Why Is AI-Based OCR Essential?
Due to such limitations of traditional OCR, companies that adopt traditional OCR require users to capture their ID documents under certain conditions.
Some examples would be like:
- Place your ID on a dark surface.
- Hold up your ID instead of placing it on a surface.
- Ensure your ID is not tilted.
The more restrictions users face, especially older demographics, the more usability is affected. When usability suffers, it directly impacts the dropout rate. If OCR fails to function properly, requiring users to submit their ID multiple times, they become frustrated and may abandon the onboarding process, which could be highly detrimental to customer acquisition and retention.
This is where AI-powered OCR proves invaluable. By learning from vast datasets, AI-based OCR can recognize diverse scripts, adapt to various font styles and conditions, and continuously enhance its performance—especially when integrated with self-learning models.
How AI-enhanced OCR can improve customer onboarding experience:
- AI-powered OCR incorporates computer vision and geometric correction algorithms that can detect, realign, and straighten skewed images before extracting text.
- Adaptive cropping and edge detection help frame the ID properly, ensuring that all relevant details are captured.
- AI-based image enhancement reduces glare, improves contrast, and sharpens text, making it readable even in low-light or overexposed conditions.
Why Partnership with a Local OCR Service Provider Matters, especially for AI-Powered OCR.
So we've seen why AI-driven OCR is crucial, especially for the usability for end users. (usability of financial services for end users.)
Unless a country uses a rather universally accepted script like the Alphabet, what works well for western ID Cards may not (more likely not) work as well for other language scripts like say - Korean Hangul. In order for scripts like Hangul to work well, the embedded AI model has to be trained on massive datasets on Korean, and especially on the Korean government issued ID Documents.
Let's dive in a little deeper here. In the case of Korea, there are 6 different types of government-issued ID Documents. And that number goes up, if we take into consideration the number of ID documents that have been updated over the years. This means there are different layouts for a singular ID Type (ex. Old and new versions of National ID Cards). Worse, the required number of datasets will increase dramatically due to the vast range of conditions in which users capture their ID documents.
So the crux of the issue is - collecting such vast and comprehensive amount of data just isn't easy-especially when it comes to datasets that include high-risk data, such as the government-issued ID documents. So instead of spending resources on trying to gather datasets that would be highly impractical and cost-inefficient to collect, working with a local partner that understands the ins and outs of all the different types of certain government-issued ID documents is a much safer and less costly option.
Seamless ID Verification Starts with the Right OCR Solution
OCR technology is the first line of interaction between businesses and users during identity verification. A high-accuracy, high-speed OCR solution not only enhances security and compliance but also optimizes customer onboarding, reducing drop-offs and ensuring a frictionless experience. In contrast, low-quality OCR can introduce delays, errors, and frustration, ultimately leading to lost customers. For businesses operating in regulated industries, investing in robust OCR technology is essential for balancing security, compliance, and user satisfaction.
Looking for a Frictionless Option? Check out Machine Eye.
Quram’s Machine Eye is the #1 Scanner chosen by 10 out of 15 Tier 1 Banks in South Korea. It’s best adapted to scan Korean Script, inherently much more complicated than the English alphabet, in mere milliseconds. By applying deep learning and other neural network models to Machine Eye, it is trained on a massive scale dataset, perfected to extract text from Korean ID Documents submitted by users under various conditions - varying angles and lighting.
Simply integrate our solution—available as Web, Mobile, Server SDKs, or SaaS—into your application or platform to meet your needs seamlessly. Don’t hesitate to book a free demo!