OCR (optical character recognition) is a technology that turns an image of text into code that can be used for data processing. Up to 90% of businesses’ data exists in unstructured formats, such as text that isn’t searchable in a database, but OCR can change that. OCR allows different types of documents, such as scanned paper documents, PDF files, and images captured by a digital camera, to become editable and searchable data. OCR invoice processing uses this technology to automate the extraction of data from digital invoices.

Below, we’ll explain the benefits of using OCR in invoice processing, how accurate and reliable OCR data extraction is, and how to integrate OCR processing into your business operations.

What’s in this article?

• How OCR technology is used in invoice management
  
• Benefits of using OCR in invoice processing
  
• How accurate and reliable is OCR data extraction?
  
• How to integrate OCR processing into your accounts payable workflow
  
• Best practices for implementing OCR
  

How OCR technology is used in invoice management

By digitising invoices and analysing the extracted data, OCR simplifies the invoice process in multiple ways. Here’s how OCR technology is used in invoice management.

Invoice digitisation

• Scanning: Physical invoices are scanned to create digital images.

• Image capture: Invoice images are captured via mobile devices or digital cameras.

Text recognition and extraction

• Optical character recognition: OCR software processes the scanned images to recognise and convert printed or handwritten text into machine-readable text.

• Field identification: The software identifies key fields such as invoice number, date, supplier name, line items, quantities, prices, and total amounts.

Data extraction

• Template-based extraction: OCR systems use pre-defined templates to extract specific data fields from invoices. This works well when invoice formats are consistent.

• Intelligent extraction: Advanced OCR systems use machine learning algorithms to recognise and extract data from different invoice formats.

Data validation

• Rule-based checks: Extracted data is validated against pre-defined rules to ensure accuracy. For example, the system might check that the invoice date is within a certain range or that the total amount matches the sum of the line items.

• Cross-referencing: OCR systems cross-reference extracted data with existing records such as purchase orders or vendor databases to verify correctness.

Financial system integration

• ERP integration: Validated data is integrated into enterprise resource planning (ERP) systems, so financial records are automatically updated with accurate information.

• Accounting software: OCR-extracted data is integrated with accounting software, automating bookkeeping tasks and reducing manual data entry.

Workflow automation

• Approval processes: OCR systems trigger automated workflows for invoice approval, routing invoices to the appropriate personnel based on predefined criteria.

• Notifications and alerts: OCR systems send automated notifications and alerts for outstanding invoices, discrepancies, or approvals.

Search and retrieval

• Searchable database: OCR systems store extracted data in a searchable database for quick retrieval of specific invoices or data points. This is particularly useful for audits, compliance checks, and financial analysis.

• Document management: OCR technology makes scanned invoices searchable by keywords, dates, or other criteria.

Analytics and reporting

• Data analysis: The OCR system’s extracted data can be analysed for insights into spending patterns, vendor performance, and other financial metrics.

• Reporting: The system can generate automated reports based on the extracted and validated data.

Benefits of using OCR in invoice processing

Using OCR for invoice processing creates the following benefits.

• Less manual work: OCR eliminates the need for employees to manually type data from invoices into accounting systems, saving time and labour costs, and freeing up staff to focus on more strategic tasks.

• Fewer errors: OCR also reduces the likelihood of errors associated with manual entry such as typos and transpositions. Avoiding these errors means avoiding costly mistakes such as incorrect payments or missed discounts.

• Faster processing: OCR makes invoice processing much faster. This creates shorter payment cycles and allows companies to handle a larger volume of invoices with the same resources and to take advantage of early payment discounts from suppliers.

• Easier compliance: Accurate data is required for compliance with financial regulations and tax laws. OCR helps businesses maintain accurate records and reduces the risk of non-compliance.

• Invoice management: OCR digitises invoices, making them easily searchable and accessible, improving visibility into the accounts payable (AP) process and facilitating audits.

• Adaptability: Modern OCR software can handle a variety of invoice formats, layouts, and even different languages, providing flexibility for businesses dealing with diverse suppliers.

How accurate and reliable is OCR data extraction?

The accuracy and reliability of OCR data extraction has improved in recent years, especially for invoice management, but there are still limitations.

Accuracy

Most modern OCR software boasts high page-level accuracy, often hitting 98% or 99% accuracy. This means that out of 1,000 characters on a page, 980–990 are recognised correctly.

But the most important measure of OCR is field-level accuracy, which is the accuracy of extracting specific data points such as invoice numbers, dates, and amounts. This is where OCR can sometimes fall short, especially if the invoice layout is complex or inconsistent, the invoice quality is poor (e.g., low-resolution scans, handwritten notes), or the OCR software is not specifically trained for invoices.

Reliability

OCR consistently extracts data in the same way, reducing the risk of human error introduced by manual data entry, but the reliability of OCR also depends on how well the software handles errors and exceptions. Some solutions to improve error handling include:

• Validation checks to flag potential errors (e.g., mismatched totals)

• Human-in-the-loop review to verify and correct extracted data

• Machine learning algorithms that improve over time

When choosing OCR software, maximise your accuracy and reliability by ensuring high-quality images, incorporating an element of human review, and choosing a solution specifically designed for invoice processing, preferably with the following features:

• Pretrained templates for common invoice layouts

• Machine learning capabilities to adapt to different formats

• Validation and error-handling mechanisms

How to integrate OCR processing into your accounts payable workflow

Here’s how to integrate OCR processing into your existing workflow.

Choose the right OCR software

• Invoice-specific OCR: Select OCR software specifically designed or optimised for invoice processing. These solutions often come with pre-trained templates for common invoice layouts and understand invoice-specific terminology.

• Specific features: Look for features such as automatic data extraction, line-item recognition, PO matching, general ledger (GL) coding suggestions, and integration capabilities with your existing accounting or ERP software.

• Deployment options: Decide whether you need cloud-based or on-premise OCR software. Cloud-based solutions are generally easier to implement and scale, while on-premise solutions offer more control and customisation.

Prepare your invoices

• Standardised format: If possible, request that your vendors send invoices in a standardised format (e.g., PDF) for easier OCR processing.

• Clear scans: Generate high-quality scans or digital images of paper invoices. Use scanners with adequate resolution and settings for optimal OCR results.

• Centralised repository: Create a centralised location (e.g., shared folder, cloud storage) to store all invoices for easy access by the OCR software.

Set up the OCR workflow

• Data extraction: Configure the OCR software to extract relevant data fields such as invoice number, vendor name, date, line items, amounts, and taxes.

• Validation rules: Implement validation rules to check for discrepancies and flag potential errors in extracted data (e.g., mismatched totals, invalid dates).

• Approval workflow: Integrate the OCR software with your existing approval workflow to automatically route invoices to the appropriate approvers based on pre-defined rules.

• Data export: Set up the OCR software to automatically export extracted data to your accounting or ERP system. This can be done through direct API integration or file exports.

Train and fine-tune the OCR model

• Initial training: Train the OCR software on a sample set of your invoices so it can recognise your specific invoice layouts and terminology.

• Continuous learning: Regularly review and correct any errors in extracted data to help the OCR model learn and improve over time.

Implement a review process

• Human-in-the-loop: Even with the best OCR technology, some level of human review is recommended to verify extracted data, especially for high-value invoices or those with complex line items.

• Exception handling: Establish a process for handling exceptions and errors flagged by the OCR software. This might involve manual review, corrections, or communication with vendors.

Monitor and optimise

• Key performance indicators (KPIs): Track KPIs such as invoice processing time, error rates, and cost savings to measure the effectiveness of your OCR implementation.

• Continuous improvement: Regularly review and refine your OCR workflow, adjusting settings and retraining the model as needed to maintain high accuracy and efficiency.

Best practices for implementing OCR

Employ the following best practices to implement OCR in your business.

Define your objectives

Determine the specific documents or processes where OCR can add the most value. Focus on areas with high manual data entry, repetitive tasks, or where accuracy is most important (e.g., invoice processing, form data extraction, document archiving). Define clear and measurable goals such as reducing processing time by a certain percentage, improving data accuracy, or achieving cost savings.

Choose the right OCR solution

Assess the volume and type of documents you’ll be processing, the required accuracy levels, and your budget. Consider features such as pre-trained templates, machine learning capabilities, language support, and integration options, and choose a solution that can scale with your needs.

Evaluate different OCR software options through demos or trials and test them with your specific document types to assess their accuracy and performance.

Prepare your documents

Ensure that your documents are scanned or captured in high resolution (300 DPI or higher) with good contrast and minimal noise. Straighten any skewed images. If possible, standardise the format of your documents (e.g., PDF/A) to make them easier for OCR software to process.

Configure and train your OCR system

If your documents have consistent layouts, create templates within the OCR software to define the areas where data should be extracted (e.g., invoice fields, form fields). If using machine learning–based OCR, provide a training set of labelled documents to teach the system to recognise different fonts, layouts, and data types. Implement validation rules to check for errors and inconsistencies in extracted data. These rules can verify that dates are valid, calculations are correct, and specific fields are not empty.

Implement a review and correction process

Establish a process for quality assurance in which humans can review and validate extracted data and use feedback from the review process to improve the OCR system’s accuracy over time. For machine learning-based OCR, this involves correcting errors in the training data and retraining the model.

Integrate with other systems

Ensure that the OCR software can export extracted data to other systems such as accounting software, databases, or document management systems. Integrate OCR with your workflow automation tools to simplify processes such as invoice approval, data entry into other systems, and document archiving.