What is Optical Character Recognition (OCR) for Identity Verification?

Incode

February 20, 2025

Optical Character Recognition (OCR) technologies for identity verification extract text from images of government-issued IDs and translate it into machine-readable data.

This technology saves people the time and hassle of manually inputting data from printed or non-editable documents or images into a digital system, while also improving accuracy, enhancing fraud detection, ensuring global compliance, and helping business to expand globally.

Top-range OCR technologies, such as those that are purpose-built, not only extract and read data faster than humans, they also make fewer mistakes.

OCR technology in the age of the telegraph

OCR technology can be traced back to the early 20th century. In 1914, physicist Emanuel Goldberg invented a machine that could read characters and convert them into telegraph code. It is considered one of the earliest examples of OCR technology.

Later, Goldberg developed what he called a “Statistical Machine”, an electromechanical machine for searching microfilm archives using an optical code recognition system. In 1931, he was granted U.S. patent number 1,838,389 for the invention. IBM promptly acquired rights to the patent.

What obstacles can basic OCR technologies face?

• Thousands of document types: Across the world, thousands of different types of identity documents are in use, with unique formats, fonts, and security features that OCR technology must classify correctly to function internationally.
• Text readability: Basic OCR technologies can struggle to recognize unusual fonts. Multiple font documents can be particularly challenging to read.
• Language limitations & special characters: OCR technologies must seamlessly switch between models for different languages, complicating recognition, especially for non-Latin scripts.
• Tricky symbols: Special service symbols (e.g., those identifying a US bank check) can be lost during data extraction by general-purpose OCR technologies.
• Confusing designs & similarities: Complex layouts can confuse basic OCR technologies; overlapping objects and insufficient contrast can lead to misinterpretation.
• Dependency on third-party developers: These technologies may be slower to adapt to changes, impacting their performance and recognition capabilities.
• Challenging environmental conditions: Poor lighting and shadows can also impact OCR accuracy.
• Poor image quality: Low-quality or blurry images can result in data misinterpretation.

What risks can arise as a result of inaccurate OCR?

• Identity fraud: Misinterpreted credentials can allow unauthorized access to systems, putting organizations at risk.
• Misinformation: Incorrect data can disrupt operations and propagate errors.
• Regulatory breaches: Inaccuracies in compliance data can lead to legal penalties.
• Disrupted business operations: Errors may require costly manual reviews.
• Poor UX & damage to reputation: Data processing errors can tarnish an organization’s reputation and erode user trust.
• Missed opportunities for global expansion: Inability to recognize international identity documents can restrict business growth.
• Data leaks: Misclassification errors may expose sensitive information.

Incode OCR technology guarantees accuracy and scalability

Incode’s purpose-built proprietary OCR technology uses machine learning to capture, classify, and process data from over 4900 global identity documents with near-perfect accuracy.
From capturing high-quality images in suboptimal conditions to parsing complex fonts, elements, and symbols, our technology is robust, scalable, and constantly evolving.

Purpose-built for global IDs

Unlike general-purpose solutions, Incode’s proprietary OCR technology is optimized for extracting and processing data from various identity documents worldwide, ensuring unparalleled accuracy.

Recognizes complex fonts & elements

Our machine learning (ML) models enhance OCR performance by adapting to document-specific variations, including complex fonts and symbols.

Built for global scalability

Our technology extracts Latin and non-Latin text from over 4900 full document types across 200+ countries with remarkable accuracy, crucial for correct data extraction.

Ensures regulatory compliance

By improving accuracy, we support compliance with regulations, mitigating risks of penalties.

Works at lightning speed

Thanks to advanced ML models, our technology outperforms humans by processing multiple frames within seconds.

Real-time feedback & image optimization

Our SDK optimizes image quality even under challenging conditions, ensuring accuracy.

Stay one step ahead

Our technology adapts quickly to new document structures, ensuring continuous improvement.

How our OCR technology works

From capture to completion, here’s how Incode’s proprietary OCR technology uses machine learning for identity verification:

Step 1: ID capture

• Quality estimation: Our ML model estimates image quality during the capture phase.
• Real-time feedback: Users receive prompts for adjustments if the image quality is low.
• Final quality check: Frames are checked by ML for acceptance.

Step 2: ID classification

• Candidate proposal: Generates a list of potential document types using a neural network.
• Refinement: Further analysis helps confirm the specific document type.

Step 3: ID OCR

• Detection: Identifies word locations on the document.
• Recognition: Uses an autoregressive language model for near-perfect accuracy.

Step 4: Barcode reader

• We use ML to enhance poor-quality barcode images, easing their reading.

Step 5: Entities extraction & representation

• Our system delivers high accuracy in identifying and processing key entities.

Drive conversions and completion rates with our streamlined workflow

Our ML models simplify user interactions, enabling efficiency and high conversion rates even under less than ideal conditions.