Optical Character Recognition
Incode’s in-house developed Optical Character Recognition (OCR) technology extracts and processes data from thousands of global identity document types, ensuring fewer data discrepancies and enhanced fraud detection.
Top global companies choose Incode for proven fraud protection that drives growth
Why Incode
Where other OCR falls short
Many general-purpose OCR technologies on the market fail to meet the requirements of fast-moving businesses.
Multiple document types
Multiple document types For global businesses, OCR technology must accurately classify and interpret a wide range of document types across all regions of operation. Achieving this requires machine learning (ML) models trained on extensive and diverse ID datasets, which many basic OCR solutions lack.
Text readability & font differences
Text readability & font differences Basic OCR technologies often struggle to recognize unusual fonts and special characters. Documents that use multiple fonts or fonts with various shapes, styles, and complexities can be particularly challenging to read when OCR systems are not trained to recognize them.
Language limitations & special characters
Language limitations & special characters Documents containing data in different languages require OCR technologies to switch between recognition models. This can be challenging when processing languages that use thousands of characters, subtle diacritical marks, or are read from right to left or vertically.
Reading tricky symbols
Reading tricky symbols Special service symbols, such as those that identify a US bank check, can be harder to read. Many general-purpose OCR technologies are not trained to detect special symbols and therefore ignore them, so key information is lost during the extraction process.
Confusing designs & document similarities
Confusing designs & document similarities Complex layouts, insufficient contrast between text and background, and obstructions like overlapping objects can result in data being misinterpreted. Plus, similar document types, such as a learner’s permit and a driving license, can confuse basic OCR technologies.
Dependency on third-party developers
Dependency on third-party developers OCR technologies that depend on third-party developers can be slower to adapt to changes, which impacts performance. These technologies can misinterpret data from government documents if they have incorporated new elements that models aren’t familiar with or capable of recognizing.
Challenging environmental conditions
Challenging environmental conditions Conditions like poor or uneven lighting, and physical document issues like shadows, stains, or tears can often reduce a top-of-the-market, general-purpose OCR system’s ability to correctly identify and interpret text. This impacts the technology’s reliability and accuracy.
Poor image quality
Poor image quality If an image is low-resolution, blurry, or distorted, it can be difficult for systems to distinguish between characters and identify certain shapes. For many basic OCR technologies, this impacts processing and leads to misread or missed text.
Our solution
The benefits of Incode OCR technology
Built in-house, Incode OCR processes 4.9K+ identity documents worldwide. It adapts to low-quality images, complex fonts, and unique symbols with accuracy and scale.
Multiple document types
Multiple document types For global businesses, OCR technology must accurately classify and interpret a wide range of document types across all regions of operation. Achieving this requires machine learning (ML) models trained on extensive and diverse ID datasets, which many basic OCR solutions lack.
Text readability & font differences
Text readability & font differences Basic OCR technologies often struggle to recognize unusual fonts and special characters. Documents that use multiple fonts or fonts with various shapes, styles, and complexities can be particularly challenging to read when OCR systems are not trained to recognize them.
Language limitations & special characters
Language limitations & special characters Documents containing data in different languages require OCR technologies to switch between recognition models. This can be challenging when processing languages that use thousands of characters, subtle diacritical marks, or are read from right to left or vertically.
Reading tricky symbols
Reading tricky symbols Special service symbols, such as those that identify a US bank check, can be harder to read. Many general-purpose OCR technologies are not trained to detect special symbols and therefore ignore them, so key information is lost during the extraction process.
Confusing designs & document similarities
Confusing designs & document similarities Complex layouts, insufficient contrast between text and background, and obstructions like overlapping objects can result in data being misinterpreted. Plus, similar document types, such as a learner’s permit and a driving license, can confuse basic OCR technologies.
Dependency on third-party developers
Dependency on third-party developers OCR technologies that depend on third-party developers can be slower to adapt to changes, which impacts performance. These technologies can misinterpret data from government documents if they have incorporated new elements that models aren’t familiar with or capable of recognizing.
Challenging environmental conditions
Challenging environmental conditions Conditions like poor or uneven lighting, and physical document issues like shadows, stains, or tears can often reduce a top-of-the-market, general-purpose OCR system’s ability to correctly identify and interpret text. This impacts the technology’s reliability and accuracy.
Poor image quality
Poor image quality If an image is low-resolution, blurry, or distorted, it can be difficult for systems to distinguish between characters and identify certain shapes. For many basic OCR technologies, this impacts processing and leads to misread or missed text.
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How it works
Our OCR technology toolkit
This guide showed how Incode’s in-house developed OCR uses ML to improve the accuracy of identity verification.
ID Capture
Incode’s in-house Fraud Lab allows us to detect and respond to new fraud vectors faster than anyone else. Our team actively monitors real-world threats such as deepfakes, synthetic identities, and injection attacks. When new patterns emerge, we quickly train and deploy model updates to stop them before they can affect our customers.
By combining live threat intelligence with proprietary biometric data and trusted government sources, the Fraud Lab delivers the highest level of identity protection across every verification workflow.
ID Classification
Incode’s in-house Fraud Lab allows us to detect and respond to new fraud vectors faster than anyone else. Our team actively monitors real-world threats such as deepfakes, synthetic identities, and injection attacks. When new patterns emerge, we quickly train and deploy model updates to stop them before they can affect our customers.
By combining live threat intelligence with proprietary biometric data and trusted government sources, the Fraud Lab delivers the highest level of identity protection across every verification workflow.
ID OCR
Our technology analyzes micro-expressions, lighting, and motion patterns, capturing the subtle cues that distinguish real users from sophisticated deepfakes.
Trained on millions of data points, this sophisticated, passive security layer is both faster and easier than conventional methods.
Barcode Reader
Our models verify device trust by issuing subtle challenges that detect tampering or injected content, ensuring only legitimate devices are used for secure verification.
Entities extraction & representation
Trained on globally inclusive data, Incode Workforce delivers near-perfect accuracy across all demographics. Whether it’s matching a selfie to an ID photo or comparing with previous captures, our technology guarantees accurate identity verification for every user.
User experience
Our streamlined workflows
Our ML models handle all the heavy lifting, making every user interaction feel effortless. By delivering improved results, even in suboptimal conditions, we save our users time and minimize the need for manual intervention.
Features such as smart frame selection, automatic ID orientation detection, and real-time feedback help to ensure our process is straightforward and easy to navigate. By simplifying and speeding up the process, we boost completion rates and drive conversions.
Highest accuracy
Proven results against leading OCR providers
Incode OCR outperforms open-source and general purpose solutions. With ongoing benchmarking and bias analysis, we ensure consistent accuracy across document types, languages, and visual conditions.
Performance analysis
Our OCR delivers higher accuracy across diverse document types, outperforming general-purpose solutions in key fields such as name, address, birthplace, document number, expiry date, and machine-readable zones (MRZ).
Benchmark comparison
In-house testing compared Incode’s OCR with Google’s general-purpose OCR. We measured exact matches across key fields from common document types. Incode consistently achieved higher accuracy.
Bias analysis
We regularly evaluate model performance across document types, languages, and visual conditions to reduce bias in recognition and ensure consistent accuracy.
Global recognition
Customers and industry leaders trust Incode
Verified reviews, certifications, and customer stories show the impact of Incode’s technology.
Top-rated by customers in G2’s Index for Identity Verification
Top-rated by customers in G2’s Index for Identity Verification
Incode’s identity verification system exceeds all expectations.
Resources
Explore more resources on
OCR and document verification
ID Verification
What is Optical Character Recognition (OCR) for Identity Verification?
Keep bad actors out with advanced AI-powered prevention. Safeguard every step of the verification journey with end-to-end fraud signal monitoring.
Read more >
Security
The History of Optical Character Recognition (OCR)
Keep bad actors out with advanced AI-powered prevention. Safeguard every step of the verification journey with end-to-end fraud signal monitoring.
Read more >
Fraud Detection
Superintelligent AI: Why Incode’s Proprietary AI Surpasses Humans in Detecting Identity Document Fraud.
Keep bad actors out with advanced AI-powered prevention. Safeguard every step of the verification journey with end-to-end fraud signal monitoring.
Read more >
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