Optical character recognition (OCR) converts images of text, be it printed, handwritten, or typed, into machine-readable data. OCR scans images or documents, reads the content, and turns it into a format that software can interpret and edit. 

Today, it is increasingly necessary to bring analogue documents into the digital world. Here, we'll explore how OCR functions, how it is used, and more. 

How Does OCR Work? 

From image to text: the basic OCR process 

From scanning passports at airports to creating books for e-readers, OCR touches almost every aspect of our lives. But what actually happens between the photo being taken and the data being digitised? In traditional OCR, the process involves four separate steps: 

1. Image acquisition. First, a camera or scanner needs to capture the information in the document. The image is then analysed, distinguishing the light background from the darker shapes (typically two-colour or black and white) of the characters.  

2. Preprocessing. Following acquisition, the image gets cleaned up. The system corrects the skew, adjusts the contrast, and strips any input that could interfere with the reading.  

3. Character recognition. The technology then identifies individual characters using either pattern matching, where it compares the shapes it sees to a library of templates, or feature extraction, an analysis of lines, curves, and intersections that make up each character. The recognised characters are then encoded into digital text. 

4. Output. The extracted information is saved in an editable and/or searchable format, ready to be used. 

As the technology has evolved to include LLMs, OCR has undergone significant changes. Modern AI-driven OCR follows a similar process to traditional OCR, but replaces pattern matching and feature extraction with neural networks that are trained on large datasets.  

Rather than comparing characters against fixed templates, these models learn to recognise text from examples they’ve learned from. This means the models are far better at interpreting different fonts, unruly handwriting, and uneven lighting.  

Traditional OCR vs. AI-Driven OCR: understanding the difference 

Of course, not all OCR is built the same, and the technology has undergone significant changes in recent years. Two types of OCR are now dominant: Traditional and AI-driven. 

Traditional OCR is the older form of the technology that relies on rule-based pattern matching. This type of OCR works best when documents are cleanly printed, when the format is recognisable, and when the fonts are such that the system can easily recognise them. However, readings can get scrambled due to bad lighting, unknown typefaces, or when documents are worn.   

AI-driven OCR, a much newer form of the technology, uses machine learning to interpret images and text. Rather than comparing shapes against a fixed or known template, it can recognise characters across a wide range of inputs, exporting information from low-resolution or damaged documents, documents with uneven lighting or cursive text, and so on.  

Examples of OCR 

As we’ve noted above, OCR is pretty much everywhere. Here are some examples of where you might encounter it, or even use it yourself:  

• Receipt scanning. Here, a TAN app reads the merchant name, date, and total from a photographed receipt 

• Digitising printed archives from physical books and newspapers to digital records.  

• Automatic number plate recognition (ANPR) used for toll systems and traffic enforcement 

• Invoice and form processing for automating financial or administrative data entry  

• Identity document reading used to extract information from a government ID such as a user’s name, date of birth, and document number or expiry date  

That last example marks the point where OCR moves from a general productivity tool into an important component of workflows for regulated entities. Let’s explore what that means for KYC and compliance.  

OCR in identity verification and compliance 

For regulated institutions like banks, fintechs, or brokerage firms, OCR plays an important role in document-based identity verification. OCR plays an important role when customers submit passport or national identity card scans, extracting the data therein. The information it extracts gets checked against external databases, and used to build an auditable record of the customer. 

All this plays an important role under Know Your Customer (KYC) and anti-money laundering (AML) regulations, where European financial institutions have to verify customer identities accurately and consistently, and with the relevant precautions in place. European Banking Authority has noted in its Guidelines on the Use of Remote Customer Onboarding Solutions that where institutions use OCR algorithms or MRZ verifications to read document data, they must ensure those tools capture information correctly. 

And the stakes of getting it wrong are especially high. OCR errors have direct consequences, with incorrect data triggering false rejections or creating compliance gaps. Poor OCR can also impact overhead for manual review, which impacts company efficiency and bottom lines. Today, AI-driven OCR, built for the variable conditions of mobile document capture, significantly reduces this risk. 

OCR software and companies 

As we can see, OCR is used across a wide range of industries and functions, including healthcare, logistics, and financial services.  

In identity verification, OCR is a critical tool, but it is rarely deployed on its own. Instead, identity verification providers and OCR companies integrate document-level OCR with additional layers, such as document authenticity checks, biometric verification, and AML screening. This is critical in terms of accuracy, and meeting the auditability standards that regulated industries require. 

Step up your document verification with Fourthline 

Fourthline's AI-driven identity verification platform combines advanced OCR with document authentication, biometric checks, and AML screening built for the accuracy and auditability that regulated industries require. If you want to see how it works in practice, get in touch with our team. 

FAQs 

What is optical character recognition (OCR)?  

Optical character recognition (OCR) is a technology that converts images of typed, handwritten, or printed text into a machine-readable format. It is used to digitise scanned documents, extract data from photographs, and much more. 

What is an example of OCR?  

OCR is used to scan passports at airports, digitise printed archives or books, and scan receipts for bookkeeping. In identity verification, OCR reads the printed data from a passport or other national ID card during onboarding to extract the customer’s name, date of birth, and document number. 

How does OCR work?  

Traditional OCR captures a document image, cleans it up, and identifies individual characters. It does this through pattern matching or feature extraction. Modern AI-driven OCR is trained on large datasets, making it far more capable of handling inconsistencies like varied fonts, languages, and varied handwriting. 

What is the difference between OCR and AI-powered OCR?  

Traditional OCR relies on rule-based pattern matching. It works well when documents are easily interpretable, clean and predictable. AI-powered OCR uses machine learning to continually improve its recognition under more complex, real-world conditions. 

What are OCR companies used for?  

OCR companies provide software or services that automate the OCR process. These types of companies might be used cases range from invoice processing and document archiving to identity verification and regulatory compliance in financial services.