# 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.