How Advanced OCR Powers Straight-Through Processing (STP)

Optical Character Recognition (OCR) is a cornerstone in the world of document processing; enabling organizations to convert masses of printed and handwritten text into machine-readable data.

As wholly automated workflows become more commonplace, the ability to quickly and accurately capture information from a wide variety of documents has become essential for maintaining efficiency. Yet, not all OCR solutions are created equal. With rapid advancement of AI and machine learning, today’s advanced OCR engines can do much, much more than basic character recognition. They deliver a level of efficiency and accuracy that traditional solutions just cannot match. And that’s why more and more companies are adopting them to secure the competitive edge.

According to Ardent Partners’ recent research, ‘AP Metrics that Matter in 2025’, 55% of organizations are upgrading to AI-enhanced OCR technologies, motivated by the fear of missing out (FOMO) on the productivity gains their rivals are already enjoying.

This article provides a high-level overview of what makes modern OCR solutions truly effective in the age of automation. In it, we’ll examine how classic and AI-enhanced OCR differ, how the technology supports straight-through processing, and share some real-world use cases and benefits. Read on to learn more. Alternatively, you can click on the links below to jump to the section of your choice:

• What is Optical Character Recognition (OCR)?
• How does OCR Work?
• What is Advanced OCR?
• Comparison of Optical Character Recognition: Traditional Vs. Advanced OCR
• Advantages of Advanced Optical Character Recognition
• Ensuring Efficient OCR Capture for Straight-Through Processing
• Advanced OCR Use Cases Across Industries
• Drive Real Growth with Advanced OCR and STP Solutions from Tungsten Automation

What is Optical Character Recognition (OCR)?

Optical Character Recognition (or OCR) describes the process of converting images of text, such as those from scanned forms or receipts, into a machine-readable format that can be edited, analyzed, or used to drive digitized workflows.

However, simply capturing and converting data isn’t enough to speed up processes. If the system can’t understand whether a string of characters represents a date, a product number, or other types of information, then manual effort is still required to validate and categorize the output — limiting OCR’s effectiveness.

Therefore, the real value of this technology comes when it’s combined with intelligent extraction logic; tech capable of interpreting text accurately and routing data to the right system for further processing.

How does OCR Work?

Classic OCR converts scanned documents and images into machine-encoded text using a three-step process:

1. Image Pre-Processing: The system cleans and prepares the scanned image for analysis. It then converts color images to black and white (binarization), reduces background graininess (noise reduction), straightens the image (deskewing), and defines page orientation, to make text easier to recognize.
2. Character Recognition: The OCR software analyzes the visual data to identify characters, by matching them against a known set of patterns in its internal database.
3. Post-Processing: The text is checked for errors and reformatted for further processing. It is here that any data errors or inconsistencies may be flagged for further review.

What is Advanced OCR?

Advanced OCR is powered by multimodal systems that combine AI, Machine Learning (ML), and Computer Vision to understand different types of data simultaneously — vastly improving the speed and accuracy of document processing.

Unlike classic optical character reader technology that relies on predefined rules and templates, advanced OCR engines leverage deep learning models trained on millions of document examples (e.g., handwritten notes, faded receipts, and low-resolution, or marked-up PDFs) to understand the document’s full context.

For instance, it can identify a table by recognizing rows and columns, a handwritten signature by its shape, or a total on a receipt based on its relative position to the other numbers, even if the format is non-standard. This contextual analysis allows AI OCR engines to correct errors based on a document’s semantic meaning and process unstructured document types without extensive training (Zero-Shot Learning).

Comparison of Optical Character Recognition: Traditional Vs. Advanced OCR

Advantages of Advanced Optical Character Recognition

While deep learning-enhanced OCR engines are powerful, they require significant computing resources, and are not always necessary for processing documents that follow familiar, structured formats. Classic OCR solutions can reliably handle these routine documents, saving both time and energy.

However, for companies handling a wide range of document types, languages, and scripts, advanced OCR provides a level of reliability and precision that’s essential for modern automated workflows. Additional benefits include:

• Ensures Process Continuity: Advanced OCR accurately captures both printed text and handwritten annotations (such as PO numbers added to invoices by hand), allowing employees to maintain familiar processes, while also gradually adapting to fully automated workflows.
• Accelerates Document Processing: The latest OCR scanning solutions have an average processing speed of 1-3 seconds per page (for structured documents), enabling high-speed, high-volume workflows.
• Enables Global Expansion: Companies can confidently explore new markets with cutting-edge OCR solutions, which support over 300 languages, including those that read from right to left (such as Arabic and Hebrew).
• Boosts Digital Transformation Efforts: Modern OCR tools seamlessly integrate Intelligent Document Processing (IDP), Workflow Automation, and AI-enhanced Knowledge Discovery solutions, to bolster digital transformation efforts while lowering Total Cost of Ownership (TCO).
• Supports Self-Service Capabilities: Due to its high degree of recognition accuracy, many advanced OCR solutions enable employees to scan receipts using their mobile devices and submit them for internal bookkeeping and reimbursement, even when the receipts are crumpled or faded.

Ensuring Efficient OCR Capture for Straight-Through Processing

Straight-through processing (STP) describes the complete automation of data capture, validation and document routing to ensure transactions move seamlessly through your systems with little to no manual intervention.

Where advanced OCR provides the foundation for pulling information from documents, STP takes this one step further by layering intelligent data validation, and business logic to ensure captured data is accurate, contextually correct, and compliant with your organization or industry’s rules.

In short, OCR determines the ‘what' when it comes to data capture, while STP addresses the ‘what next’ in document process automation.

Recent research suggests that STP can reduce processing time by 82% and lower operational costs by 80%.

However, to achieve these results, OCR-integrated automation platforms must be designed to support best practices for maximizing OCR and STP efficiency. Key considerations include:

• Starting With High-Quality Images: Use crisp, clear document scans. Wherever possible, also work with digital PDFs for data consistency.
• Adjust OCR Controls: Focus on extracting only essential fields and let secondary fields be inferred from validated master data to streamline workflows.
• Customize Automation Levels: Tailor automation to suit departmental needs. Some teams may need to flag every inconsistency, while others may benefit from greater automation capabilities with fewer alerts and subsequent manual checks.
• Automate Master Data Updates: When new or corrected data is approved, ensure it is updated directly in the master record, eliminating the need for manual updates.

Advanced OCR Use Cases Across Industries

Accounts Payable (AP) and Accounts Receivable (AR) Automation

Across all industries, solutions like Tungsten AP Essentials deliver AI-enhanced OCR that continuously learns from every invoice it processes, steadily improving invoice automation capabilities over time. Plus, we guarantee your system stays current through regular updates and proactive maintenance, resulting in Tungsten OCR consistently achieving 95+% invoice processing accuracy. This translates to:

• Faster processing times (from days to minutes)
• Reducing invoice processing costs by up to 80%
• Dramatic reduction in errors (from 10% to 1-2% or less)

Now let’s take a look at some real-world advanced OCR use cases across various industries:

Retail and Service Industries

• Improve “Touchless” Invoice Processing: As a leading multi-technical services provider, SPIE Netherlands uses Tungsten OCR to increase invoice recognition accuracy from 50% to 75%. This, in turn, halves the time it takes for the company to process a single invoice, while also enabling finance teams to improve supplier collaboration.
• Customer onboarding: OCR technology improves customer onboarding and order processing by automatically digitizing and extracting key details from incoming orders. This helps consumer electronics companies, such as SMEG, eliminate manual workflows and free up in-house teams to focus on higher-value tasks like improving customer experiences.

Services financiers

• Loan Origination: OCR technology can digitize and analyze a variety of documents (from bank statements to tax forms) to validate key financial data for loan applications. For example, Alliance Bank uses this approach to expedite loan processing cycles, allowing sales personnel to cut manual data entry time by 20%.
• Claims Fraud Detection: Advanced OCR capabilities have been utilized by insurers like Aviva to extract key details from accident reports, enabling analysts to rapidly identify and prevent potential instances of fraud. This approach resulted in the company saving £113 million in settlement costs throughout 2020.

Santé

• Digitize Patient Intake Forms: Advanced OCR can accurately capture information from handwritten or scanned forms, helping to improve patient outcomes. For instance, Bendigo Health, a healthcare provider in Australia, used advanced document scanning and workflow automation solutions to digitize 705,000 patient records, enabling it to deliver 300 clinical records daily to its medical teams.

Government and Public Sector

• Case Management: As global leaders in case management, Serco uses advanced OCR to compare information from scanned documents with data from government databases. This enables them to meet stringent government SLAs for data processing speed and accuracy, analyzing approximately 40 million document pages per year, without increasing operational complexity.

Drive Real Growth With Advanced OCR and STP Solutions from Tungsten Automation

In summary, the evolution from classic to advanced OCR has transformed document processing from simple character recognition to intelligent end-to-end automation.

Modern solutions like Tungsten AP Essentials combine AI, business logic, and flexible exception handling, to provide the first vital step in STP workflows — ensuring accuracy, efficiency, and control, even in the most challenging document recognition scenarios.

So, if you’re ready to take the next step in document automation, explore more of our learning resources or contact us to see how our OCR solutions can transform your business.