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

Across the world, thousands of different types of identity documents are in use, and they all have their own formats, fonts, and security features. For businesses to be able to operate internationally, the OCR technology they use has to be able to successfully classify the wide range of document types that exist in the regions and countries in which they are active, and accurately extract and interpret the data that each one contains. To do this, their ML models need to be trained on large sets of IDs, which is not the case with top-of-market general-purpose OCR technologies.

Basic OCR technologies can struggle to recognize unusual fonts. Documents that use multiple fonts can be particularly challenging to read.

Documents containing data in different languages require OCR technologies to seamlessly switch between recognition models, which can prove challenging.

Non-Latin scripts, such as Arabic or Chinese, can be harder to recognize due to their larger character sets, intricate shapes, contextual variations —some letters change shape depending on their position in a word— and subtle differences between characters.

While Latin-based alphabets typically have 26 characters —plus additional diacritics—, some non-Latin languages have thousands of characters. Chinese has over 50,000 characters, with around 8,000 in common use. Japanese is a particularly complex language as it combines three scripts: Kanji (Chinese characters), Hiragana, and Katakana. Even when processing Latin-based languages, subtle diacritical marks can be misread by basic OCR technologies.

Some non-Latin scripts are written from right to left or vertically, which can confuse basic OCR technologies that have been trained to process information from left to right.

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 read special symbols and therefore ignore them, meaning the information they contain is lost during the extraction process.

Complex layouts with multiple columns, tables, or a mix of text and images can confuse basic OCR technologies. Insufficient contrast between text and background can also affect their performance. Obstructions such as overlapping objects can result in data being misinterpreted. Similar document types, such as a learner’s permit and a driving license, can confuse basic OCR technologies.

OCR technologies that depend on third-party developers can be slower to adapt to changes, which impacts performance. These OCR technologies might misinterpret data from government documents on account of them having incorporated new elements that models aren’t familiar with or capable of recognizing.

Poor lighting and shadows can impact accuracy.

Low-quality, blurry images can cause OCR technologies to misinterpret data. Stains can also impact accuracy.

Misinterpreted credentials or failure to detect counterfeit or tampered identity documents can result in unauthorized individuals gaining access to systems, areas, and services, putting people and organizations at risk of being exposed to criminal activity or dangerous behaviour.

Incorrect or incomplete data extraction can spread misinformation, propagate errors, and disrupt business operations. Decisions made in response to analysis of defective data can harm your organization.

When extracting data for compliance purposes —such as KYC (Know Your Customer) requirements in banking—, inaccuracies can result in regulatory breaches, litigation, and criminal penalties.

Poorly interpreted data can require extensive manual review, which is time-consuming and costly. Errors in extracting data slow down business operations.

Data processing errors can damage your organization’s reputation, result in user dissatisfaction and loss of trust, and attract negative media attention. Errors such as false rejections —when OCR technology incorrectly flags valid documents as invalid— can frustrate users. If the OCR process is slowed down by complex conditions, languages, and layouts, this can impact the user experience and increase drop-off rates.

The inability to recognize non-Latin languages and updates to international identity documents can hinder opportunities to expand globally and reach international clients or customers.

Errors in data classification could expose sensitive data.

Unlike general-purpose solutions, purpose-built technologies are optimized for a specific task. In our case, Incode’s proprietary OCR technology is purpose-built to extract and process data from thousands of different types of identity documents from around the world, guaranteeing near-perfect accuracy.

In fact, Incode has been shown to outperform Google’s general-purpose OCR solution in extracting and interpreting data from key fields on identity documents such as name, expiry date, and document number.

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

Our proprietary OCR technology extracts Latin and non-Latin based text from over 4900 full document types from 200+ countries and territories with unparalleled accuracy. Our 2-stage algorithm for classifying document types from around the world plays a key role in ensuring correct data extraction.

By improving accuracy, we help to ensure that your organization complies with industry regulations, which could otherwise result in penalties or reputational damage.

Thanks to our powerful machine learning (ML) models and adaptability, our capture SDK outperforms humans by evaluating multiple frames within seconds.

Our capture SDK employs smart frame selection and provides real-time feedback to optimize image quality. This ensures accuracy and completeness, even on low-end devices or in poor visual conditions. Our intelligent system will automatically detect the ID orientation during the capture phase.

Our pipelines are tailored for different document types, ensuring we can quickly adapt to new document structures, layouts, and security elements. Our in-house team of developers act fast when updates are required and our in-house team of reviewers keep our database constantly updated.

Our two-stage classification system ensures precise document identification.

Next, our Optical Character Recognition (OCR) technology extracts and interprets the text that appears on the document. This phase is split into two stages: detection and recognition.

Barcodes contain valuable user information, yet they can be challenging for OCR technologies to read. To mitigate this issue, we developed a ML model that restores and enhances poor-quality images of barcode to make them easy to read.

We deliver near-perfect accuracy when identifying and extracting key entities like names, addresses, and document numbers. Our system processes the extracted text into structured data, accounting for differences in date formats, shifting field positions, and document-specific rules (like front vs. back address stickers).