Optical Character Recognition (OCR) is a widely-used technology that converts text and numerical data from images and other scanned or photographed documents into machine-readable, editable digital text.
Optical Character Recognition has become an important tool in many different applications that require digitisation of paper documents and printed media. By transforming static images into structured text data, OCR effectively eliminates the need for manual data entry, reduces errors↗, and supports automation in all sorts of key workflows.
Optical Character Recognition is particularly important for document management systems, accounting software, and expense management platforms.
What is Optical Character Recognition (OCR)?
Optical Character Recognition is the process of identifying printed or handwritten characters within an image and converting them into text that can be read, searched, and processed by digital systems.
OCR software achieves this by analysing the visual patterns of letters, numbers, and symbols, and then translating them into digital characters. These extracted characters can be stored, edited, or integrated into other applications.
Without OCR this would have to be done manually, which is slow, expensive, and prone to human error. This is especially true in high-volume financial environments, where OCR has become a crucial part of day-to-day workflows.
How does OCR work?
OCR works by analysing images at the pixel level and identifying patterns that correspond to characters. Many modern OCR solutions use artificial intelligence and machine learning to augment this process and improve accuracy. The typical OCR workflow includes the following stages.
Preprocessing
First the image is prepared for analysis by improving quality and clarity. Common preprocessing steps include. This may involve converting images to greyscale, removing noise and background artifacts, correcting image rotation or skewing, and enhancing contrast and brightness. Each of these steps help improve OCR accuracy by clearly defining characters.
Segmentation
Next the OCR system separates text from non-text elements. This can be things like images, tables, or borders. Text is then divided into lines, words, and individual characters.
Character recognition
The software then analyses each character against known patterns. Where traditional OCR relied on template matching, modern Optical Character Recognition uses machine learning models trained on many different fonts, layouts, and languages.
Post-processing and transcription
Finally, recognised characters are converted into machine-readable text. During this process, dictionaries, language models, and contextual rules are applied to correct spelling, format numbers, and ensure accurate interpretation of dates, currencies, and totals.
Types of OCR
Different OCR technologies may be used depending on the complexity of the document and accuracy required:
- Standard OCR: Extracts printed text from clean, structured documents
- Handwritten OCR (ICR): Recognises handwritten characters, often with lower accuracy
- Zonal OCR: Extracts data from predefined areas, such as invoice totals or IBAN fields
- Multilingual OCR: Supports multiple languages and character sets
What are the main use cases of OCR?
Optical Character Recognition is used across many industries, but it is particularly valuable in finance, accounting, and operations.
Financial document processing
OCR extracts data from invoices↗, receipts, and bank statements, enabling automated bookkeeping, invoice processing, and reconciliation↗.
Expense management
OCR allows expense management software to automatically capture merchant names, amounts, tax values, and dates from receipts↗.
Document digitisation and archiving
Businesses use OCR to convert paper documents into searchable digital files, improving accessibility and reducing storage costs.
Compliance and auditing
OCR enables quick retrieval of financial records and supporting documents during audits, tax reviews, and regulatory checks.
Identity verification and onboarding
OCR extracts personal data from passports, ID cards, and driver’s licences as part of KYC and onboarding workflows.
How did OCR originate?
The origins of Optical Character Recognition date back to the early 20th century, when early machines were developed to recognise printed text for accessibility purposes.
In the 1950s and 1960s, OCR gained commercial relevance through applications such as cheque processing and mail sorting in banks and postal services. These early OCR systems were limited to specific fonts and layouts.
Advances in computing power, scanners, and machine learning have since transformed OCR into a highly flexible and accurate technology capable of processing complex document layouts and multiple languages.
Why is Optical Character Recognition important?
OCR is important because it converts static, unstructured information into usable digital data. Many business-critical documents still originate as paper or image files, especially in finance and accounting.
Key benefits of OCR include:
- Reduced manual data entry and processing time
- Lower operational costs
- Improved data accuracy and consistency
- Searchable and structured document data
- Scalable automation for growing document volumes
In financial workflows, OCR often serves as the first step toward end-to-end automation.
OCR vs. Intelligent Document Processing (IDP)
Optical Character Recognition focuses on extracting text, while Intelligent Document Processing (IDP) goes further by interpreting and validating that text.
IDP combines OCR with machine learning, natural language processing, and business rules to classify documents, validate extracted data, and trigger automated workflows↗. In practice, OCR provides the raw text data that IDP systems build upon.
Limitations of OCR
Although OCR technology is highly effective, accuracy can be affected by:
- Poor image quality or low-resolution scans
- Unusual fonts or handwriting
- Complex layouts or inconsistent formatting
For this reason, OCR is often paired with validation logic or AI-based processing layers in financial systems.
Summary
Optical Character Recognition (OCR) is a technology that converts text from images and scanned documents into machine-readable data. By reducing manual data entry and enabling automation, OCR plays a key role in modern document processing, financial operations, and digital workflows.