Driving End-to-End Transformation with Optical Character Recognition Automation
Manual, repetitive tasks have long plagued enterprise teams, especially when it comes to processing unstructured data like invoices, claims, or scanned forms. This is where Optical Character Recognition (OCR) automation comes in. When combined with Robotic Process Automation (RPA), it becomes a powerful tool that enables seamless, intelligent workflows that extend across departments and systems.
In this blog, we explore how OCR automation enhances RPA capabilities using real-world examples from Fortude’s own automation implementations across finance and CRM functions. If you are looking to understand document-heavy processes and increase enterprise efficiency, this blog is for you.
What is OCR automation and why does it matter?
Optical Character Recognition automation uses tools to extract data from scanned or digital documents and conversion into structured or unstructured formats. When paired with RPA bots, OCR automation enables enterprises to automate end-to-end processes that involve unstructured data sources.
Invoices, supplier POs, or customer claim forms typically arrive in non-machine-readable formats like PDFs or images. OCR automation is able to extract information in these hard-to-read formats, making the process of accessing data much more efficient.
| Pro tip: When selecting an OCR tool, prioritize one that integrates natively with your RPA platform and supports custom ML model training for your specific document types. |
The power of synergy: OCR + RPA
While RPA can mimic rule-based actions across systems, it can’t independently read scanned text or handwritten content. OCR fills this gap by:
- Digitizing physical/digital documents
- Extracting key data fields (e.g., invoice number, PO number, claim category)
- Passing clean data to RPA bots for validation, entry, and processing
Fortunately, this results in end-to-end automation where human intervention is only required in exceptions.
| Pro tip: Always monitor extraction confidence levels from your OCR engine and set up human validation steps in the process to ensure business rule integrity. |
Real-world use case #1: Automating customer claims creation in SAP CRM
In one implementation, Fortude helped a global enterprise automate CRM claims creation using OCR and RPA in UiPath.
Process overview:
- Customers submit claims via email or shared folders
- OCR automation extracts fields like invoice number, SKU, total amount
- RPA bots validate against SAP ECC records
- Valid claims are bulk uploaded into SAP CRM
Highlights:
- Document Understanding (DU) powered by a custom ML model
- Bulk upload via CRM templates
- Audit logs and exception emails for traceability
Impact: Reduced claim turnaround time and manual errors and increased scalability during peak volumes.
Real-world use case #2: Accounts payable automation with 2-way matching
In Fortude’s AP automation use case, OCR and RPA work together to streamline invoice processing in SAP.
Process flow:
- Bot retrieves invoice PDFs from a monitored email inbox
- OCR automation extracts key fields (invoice number, PO number, vendor details)
- Data is matched against SAP PO records
- If matched, invoice is posted via BAPI integration
- If not, routed to action center for human validation
Key benefits:
- Exception routing and business rule validation
- Full integration with SAP ECC
- 90-second processing time per invoice
Impact: Enabled processing of over 200 invoices daily with reduced FTE effort and improved compliance.
Real-world use case #3: From PO to invoice automation
This use case involved end-to-end automation from purchase requisition creation to invoice dispatch:
Key steps:
- OCR automation extracts invoice details
- RPA bot creates a PR in SAP
- Upon approval, PO is created and added to invoice PDF using PDF nuance
- Final invoice is sent to AP for payment
Special features:
- ML model retrained using vendor-specific invoices
- Hybrid handling of both DU-extracted and human-prepared invoices
- Daily orchestration scheduling and queue monitoring
Impact: Faster invoice lifecycle with built-in validations and a unified performer process for all invoice types.
Why OCR automation is essential for scalable RPA
| Benefit | Description |
|---|---|
| Unstructured data processing | Enables bots to handle PDFs, scans, and handwritten forms |
| Increased accuracy | ML-powered extraction reduces manual entry errors |
| Scalability | Easily adapts to new document formats and business rules |
| Auditability | End-to-end logs and exception reports for compliance |
| Reduced human touchpoints | Staff work on only high-risk exceptions |
Best practices for integrating OCR automation with RPA
- Use domain-trained ML models for high accuracy (e.g., invoices, claims)
- Design a human-in-the-loop validation process for exceptions
- Log all steps for transparency and audit readiness
- Avoid hardcoded data and use orchestrator-managed assets
- Plan for business continuity (BCP) in case bots are offline
| Pro tip: Define fallback mechanisms like email alerts or manual upload processes during planned or unplanned automation downtimes. |
How Fortude helps enterprises adopt OCR + RPA
Fortude’s Intelligent Automation solutions integrate OCR, RPA, and Document Understanding to solve complex business problems. From CRM claims to AP processing, our delivery teams have deep expertise in the UiPath ecosystem, SAP integration, and domain-specific ML model customizations.
Ready to transform document processing?
With OCR automation fueling your RPA strategy, your enterprise can eliminate glitches and build future-ready workflows. Whether it’s claims, invoices, or contracts, Fortude can help you digitize, automate, and scale. Get in touch with our automation specialists today.FAQs
OCR (Optical Character Recognition) and RPA (Robotic Process Automation) are complementary technologies often used together in intelligent automation. OCR focuses on reading and extracting text from documents converting it into machine-readable data. On the other hand, RPA takes that structured data and performs tasks such as entering it into systems.
Yes, modern OCR tools, including advanced solutions like UiPath Document Understanding (DU), are capable of extracting text from handwritten documents. These tools use AI and machine learning models trained in diverse handwriting styles. However, the accuracy of handwriting recognition depends on several factors such as the legibility of the writing, quality of the scanned image, and how well the model has been trained.
Human validation is not always required, especially when the OCR model performs with high accuracy and confidence. However, in real-world scenarios, certain fields may not be confidently extracted, or specific business rules may fail validation. In these cases, a “human-in-the-loop” approach is recommended. This allows a human reviewer to intervene, correct errors, and confirm extracted values, ensuring data quality and compliance. Over time, this feedback can also be used to train and improve the OCR models for better automation outcomes.
Industries that deal with large volumes of structured and unstructured documents stand to gain the most. Because it reduces manual data entry, improves accuracy, and speeds up workflows. The industries include finance, logistics, manufacturing, retail, and healthcare. OCR helps these sectors drive operational efficiency and achieve significant ROI.
Fortude distinguishes itself by offering a unique blend of deep domain expertise, ERP knowledge, andtechnical proficiency across major automation platforms. Unlike generic automation vendors, we understand the intricacies of industries like manufacturing, retail, healthcare, and logistics. This allows us to design tailored automation solutions that align with business goals. Our approach emphasizes scalability, compliance, andlong-term value. This industry-first mindset ensures we deliver not just automation, but real business transformation.
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