Accounts payable

Detailed guide on OCR invoice processing in accounts payable

Sep 13, 2024

In today's fast-paced business environment, efficiency and accuracy in managing financial transactions are paramount.


The traditional method of handling invoices, which involves manual data entry, is not only time-consuming but also prone to errors. This is where OCR software invoice processing technology comes into play, revolutionizing invoice processing by automating data extraction.


OCR software invoice processing technology converts different types of documents, such as scanned paper invoices, PDFs, or images, into editable and searchable data. By automating invoice processing, businesses can reduce manual effort, lower operational costs, and improve the overall accuracy of their accounts payable operations.


With the growing need for digital transformation, OCR software invoice processing is increasingly becoming a vital tool for businesses aiming to optimize their financial processes and enhance productivity.


The adoption of OCR technology not only accelerates invoice processing but also provides a more systematic approach to managing financial data, making it easier to maintain accurate records and ensure compliance with accounting standards.


Additionally, this technology enables real-time data analysis, further empowering businesses to make informed financial decisions and adapt swiftly to market changes.

What is invoice OCR technology?


Invoice OCR technology, or Optical Character Recognition for invoices, is a digital solution designed to automate the extraction of data from paper or digital invoices.


This technology leverages advanced algorithms to identify and convert printed or handwritten text on invoices into machine-readable data. Invoice OCR eliminates the need for manual data entry, reducing the time and effort required to process large volumes of invoices.


By using OCR, businesses can automatically capture essential details such as invoice number, date, vendor information, line-item details, and amounts due. This automated process not only accelerates invoice processing but also minimizes the risk of human error, ensuring greater accuracy in financial records.


Additionally, OCR technology can be integrated with various accounting and enterprise resource planning (ERP) systems, providing a seamless workflow for managing accounts payable.


Overall, invoice OCR technology is a crucial tool for modernizing financial operations and enhancing the operational efficiency of modern-day businesses across the globe.

How does OCR automate invoice processing?


Manual invoice processing involves several steps, including sorting, data entry, validation, and approval, each of which can be time-consuming and prone to errors.


Traditionally, invoices are manually sorted and data is entered into accounting systems by hand. This process increases the likelihood of errors due to manual entry.


Invoice OCR technology automates a significant portion of this workflow by converting scanned or digital invoices into editable and searchable data.


For example, companies use OCR to streamline their accounts payable processes by automatically extracting key information from invoices, such as vendor names, invoice numbers, dates, and amounts. The extracted data is then validated against predefined rules and checked for accuracy, reducing the need for manual verification.


By automating these steps, OCR technology significantly reduces the time and resources needed for invoice processing, allowing businesses to focus on more strategic tasks and improving overall efficiency.


Additionally, the integration of OCR technology with existing AP and ERP systems is vital for optimizing workflow efficiency, similar to how virtual cards can be used to streamline accounts payable processes.


As a result, businesses can achieve faster turnaround times for invoice approvals and payments, ultimately leading to improved vendor relationships, cash flow analysis and management.

What are the challenges faced with manual invoice processing?


Time-consuming paper-based process


Manual invoice processing requires handling paper documents, which involves sorting, organizing, and storing physical invoices.


This paper-based approach is time-consuming and inefficient, especially for businesses dealing with a high volume of invoices.


It also increases the risk of lost or misplaced documents, further delaying the processing cycle.


Prone to human error


Human error is a common issue in manual invoice processing.


Errors can occur during data entry, such as incorrect amounts or mismatched invoice details, leading to discrepancies in financial records.


These errors can result in delayed payments, strained vendor relationships, and increased costs for correcting mistakes.


Long approval delays


Manual invoice processing often involves multiple approval stages, each requiring time and coordination between departments.


Delays in getting approvals can slow down the entire invoice processing workflow, leading to late payments and potential penalties.


This lack of efficiency can negatively impact cash flow management and vendor relationships.


High costs


The manual processing of invoices is labor-intensive and requires significant human resources, leading to high operational costs.


Businesses incur additional costs related to paper storage, document handling, and administrative tasks.


Over time, these costs can add up, reducing the overall profitability and efficiency of the accounts payable process.


Matching issues


Manual invoice processing can result in matching issues, such as discrepancies between purchase orders, receipts, and invoices.


These mismatches can cause payment delays and require additional time for investigation and resolution.


Effective invoice matching is crucial for maintaining accurate financial records and ensuring compliance with company policies.


Difficulty in scalability


As businesses grow, the volume of invoices increases, making it challenging to scale manual invoice processing operations.


The manual approach requires additional personnel and resources to handle the increased workload, which is not always feasible.


This lack of scalability can hinder business growth and limit operational efficiency.


Inconsistencies with data


Manual data entry is prone to inconsistencies, especially when multiple people are involved in the process.


These inconsistencies can lead to inaccurate financial reporting, making it difficult for businesses to maintain precise accounts payable records.


Regular audits and checks are required to identify and correct these discrepancies.


Duplicate payments


Manual invoice processing increases the risk of duplicate payments, especially when multiple copies of the same invoice are submitted or when data entry errors occur.


Duplicate payments can lead to financial losses and necessitate additional effort to identify and rectify the issue, further complicating the accounts payable process.

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How does OCR invoice processing work?


OCR invoice processing involves several steps to accurately extract data from invoices.


The extracted data is validated against predefined rules and checked for accuracy, streamlining the invoice-to-pay process and reducing the need for manual verification.


This process converts paper or digital invoices into editable and searchable formats, enabling automated data entry into accounting systems.

1

Uploading a digital invoice

The first step in OCR invoice processing is uploading a digital copy of the invoice into the OCR software.


This can be done by scanning a paper invoice or uploading a digital file, such as a PDF or image.


The OCR software then analyzes the digital copy to prepare it for data extraction.


The accuracy of the extraction relies on the quality of the initial upload.

2

Image enhancements

To ensure accurate data extraction, the OCR software applies image enhancements to the digital invoice copy.


These enhancements may include adjusting brightness and contrast, removing noise, and correcting distortions.


Improved image quality helps the OCR software accurately recognize and capture text from the invoice, reducing errors and improving overall accuracy and efficiency.

3

Text recognition

After enhancing the image, the OCR software identifies and recognizes the text on the invoice.


The software uses advanced algorithms to distinguish between text, images, and other elements, converting the recognized text into machine-readable characters.


This step is crucial for accurately capturing invoice details such as vendor names, invoice numbers, dates, and amounts.

4

Extracting data

Once the text is recognized, the Optical Character Recognition software extracts relevant data from the invoice.


This data includes key information such as invoice number, date, vendor details, line-item descriptions, quantities, prices, and total amounts.


The extracted data is then formatted and structured for easy integration with accounts payable systems.

5

Validating data for accuracy

To ensure the accuracy of the extracted data, the Optical Character Recognition (OCR) software performs a validation check.


This step involves comparing the extracted data against predefined rules or templates to identify any discrepancies or errors.


Data validation helps ensure that all invoice details are correctly captured and reduces the need for manual review.

6

Exception handling

In cases where the OCR software encounters unclear or ambiguous information, exception handling is required.


The software flags these exceptions for manual review, allowing users to verify and correct any errors.


Exception handling ensures that all data is accurately captured and reduces the risk of errors in the accounts payable process.

7

Exporting data

After the data is extracted and validated, the OCR software exports it to the accounts payable system or other relevant software.


This step automates the data entry process, reducing the need for manual input and speeding up the overall invoice processing workflow.


Additionally, this automated export minimizes the risk of data duplication and inconsistencies, ensuring data accuracy.

8

Archiving data

Finally, the processed invoice data is archived for future reference and compliance purposes.


The OCR software stores digital copies of invoices and associated data in a secure, searchable database, making it easy to retrieve documents and maintain accurate financial records.


This digital archiving also facilitates efficient auditing and reporting, to ensure transparency and adherence to regulatory requirements.

What are the OCR invoice processing methods?


Templates-based OCR


Templates-based OCR relies on predefined templates to recognize and extract data from invoices.


Each template corresponds to a specific invoice format, allowing the Optical character recognition (OCR) software to accurately capture data based on known patterns.


While effective for standardized invoices, template-based OCR may struggle with varying or new invoice formats.


Rule-based OCR


Rule-based OCR uses a set of predefined rules to identify and extract data from invoices.


These rules specify the locations and formats of key invoice details, such as vendor names, invoice numbers, and amounts.


Rule-based OCR is flexible and can handle various invoice formats, but requires regular updates to maintain accuracy.


Cloud-based OCR


Cloud-based OCR leverages cloud computing resources to perform data extraction and processing. This method offers scalability, flexibility, and ease of integration with other cloud-based systems.


Cloud-based OCR solutions are often subscription-based, providing businesses with access to advanced OCR technology without the need for extensive on-premise infrastructure.


Additionally, these solutions allow for real-time updates and continuous improvements.


Machine learning-based OCR


Machine learning-based OCR uses artificial intelligence algorithms to improve data extraction accuracy over time.


These algorithms learn from past data and errors, refining their ability to recognize and extract information from diverse invoice formats.


Machine learning-based OCR is highly adaptable and can handle complex invoices with varying layouts and formats.


On-premise OCR


On-premise OCR solutions are installed locally on a company’s servers and hardware.


This method provides greater control over data security and privacy, as all data processing occurs within the organization’s infrastructure.


On-premise OCR (Optical Character Recognition)is suitable for businesses with strict data governance policies but requires significant initial investment and maintenance.

Types of accounts payable documents that can be extracted using OCR


1. Invoices


Invoices are the most common type of accounts payable document processed using OCR technology.


OCR software extracts key details such as invoice number, date, vendor information, line-item descriptions, quantities, prices, and total amounts, streamlining the data entry process and reducing manual effort in managing accounts payable.


2. Receipts


OCR software can process receipts, capturing information such as date, vendor name, items purchased, quantities, prices, and total amounts.


Automating receipt processing reduces the time spent on manual data entry and enhances expense tracking, providing businesses with better visibility into their spending patterns.


3. Tax documents


Tax documents, such as tax invoices and credit notes, can be processed using OCR technology.


The software extracts essential data, including tax identification numbers, invoice numbers, dates, and amounts, ensuring accurate tax reporting and compliance with regulatory requirements.


Automating tax document processing helps businesses maintain accurate tax records.


4. Payment remittances


OCR technology can extract data from payment remittances, such as payment dates, amounts, and references to invoices being paid.


Automating the extraction of remittance data improves cash flow management and ensures timely reconciliation of payments against outstanding invoices, reducing the risk of discrepancies in accounts payable.


5. Purchase orders


OCR technology can also extract data from purchase orders, including order numbers, dates, vendor details, item descriptions, quantities, and prices.


Automating the extraction of purchase order data simplifies the reconciliation process between invoices and purchase orders, improving accuracy and efficiency in accounts payable operations.


6. Expense reports


OCR software can process expense reports by extracting information such as employee names, dates, expense categories, amounts, and descriptions.


Automating expense report processing reduces manual effort, improves accuracy, and streamlines reimbursement workflows, enabling businesses to efficiently manage employee expenses and maintain accurate financial records.


7. Credit memos


Credit memos, which represent adjustments or refunds issued by vendors, can be processed using OCR technology.


The software extracts relevant details such as credit memo numbers, dates, vendor information, and amounts, helping businesses accurately record and manage adjustments in their accounts payable systems.

Importance of OCR invoice processing in accounts payable

Allows automation of invoices

OCR invoice processing enables the automation of invoice data entry, reducing the need for manual input.


This automation accelerates the invoice processing workflow, minimizes human errors, and frees up valuable time for accounts payable teams to focus on higher-value tasks.


By automating repetitive tasks, businesses can improve efficiency and reduce costs.


Additionally, increased efficiency allows for faster invoice approvals and payments, enhancing vendor relationships and optimizing cash flow management.

Converts handwritten text to digital invoices

OCR technology can convert handwritten text on invoices into digital data, making it easier to process and manage.


While handwriting recognition is challenging, modern OCR solutions offer accuracy, reducing the need for manual data entry and verification.


Automating the conversion of handwritten invoices enhances data consistency and speeds up the processing time.


Additionally, this capability helps businesses efficiently handle diverse invoice formats and sources, further streamlining their accounts payable operations.

Automatically extracts information from images

Optical Character Recognition (OCR) software can automatically extract information from scanned images of invoices, capturing key details such as invoice numbers, vendor names, dates, and amounts.


This automated extraction reduces manual data entry and ensures that all relevant information is accurately captured, improving the efficiency and accuracy of the accounts payable process.


Additionally, the automation of data extraction facilitates faster invoice processing, enabling timely payments and enhancing overall financial management.

Produces scanning codes on invoices

OCR technology can generate scanning codes, such as barcodes or QR codes, on invoices for easy tracking and retrieval.


These scanning codes significantly streamline the invoice processing workflow, enabling quick access to invoice data.


This, in turn, reduces the time spent searching for specific documents.


This feature enhances overall efficiency in managing accounts payable and further integrates with other digital systems to ensure seamless data flow and accessibility.

Allows faster processing of invoices

OCR invoice processing significantly speeds up the time it takes to process invoices, from receipt to payment.


By automating data extraction and validation, OCR technology reduces the need for manual intervention, accelerates approval workflows, and ensures timely payments to vendors.


Faster invoice processing improves cash flow management and vendor relationships.


Additionally, it allows finance teams to reallocate resources to strategic tasks, further enhancing overall business efficiency.

Ensures a systematic digital filing system

OCR technology creates a systematic digital filing system for invoices and related documents.


Digital storage makes it easy to retrieve and manage digital and electronic invoices, reducing the reliance on physical storage space and minimizing the risk of lost or misplaced documents.


A digital filing system also supports compliance by ensuring accurate and accessible records.


Furthermore, the digital nature of these files allows for seamless sharing and collaboration.

Boost efficiency with Volopay’s OCR technology

What are the benefits of OCR invoice processing of accounts payable?


Increased efficiency and time savings


OCR invoice processing automates data entry and validation, significantly reducing the time required to process each invoice.


This automation streamlines the accounts payable workflow, allowing businesses to process more invoices in less time and allocate resources to higher-value activities, ultimately boosting overall efficiency.


Furthermore, it provides real-time visibility into invoice status and financial metrics, enhancing decision-making and strategic planning.


Lower risk of errors


By automating data extraction and entry, OCR technology minimizes the risk of human errors associated with manual invoice processing.


Automated data validation further ensures the accuracy of captured information, reducing discrepancies in financial records and improving the reliability of accounts payable operations.


This leads to more consistent and reliable financial reporting, enhancing trust in the accuracy of financial statements.


Cost savings


Automating invoice processing with OCR technology reduces the need for manual labor and minimizes operational costs associated with paper-based processes, storage, and administrative tasks.


By reducing errors and speeding up processing times, OCR technology helps businesses save money and improve their bottom line.


Additionally, the reduction in paper and physical storage needs contributes to environmental sustainability and further cost reductions.


Improved data accuracy


OCR technology enhances data accuracy by automating the extraction and validation of invoice information.


This automation reduces the likelihood of errors and discrepancies in accounts payable records, ensuring more accurate financial reporting and better decision-making for the business.


Enhanced accuracy also minimizes the risk of financial penalties and compliance issues due to incorrect data.


Digitizes invoice archiving


OCR software digitizes the archiving of invoices, making it easier to store, search, and retrieve documents.


Digital archiving reduces reliance on physical storage, minimizes the risk of lost or misplaced documents, and ensures that invoices are easily accessible for audits and compliance purposes.


This digital approach also facilitates faster retrieval of historical data for trend analysis and financial reviews.


Boosts accounting efficiency


By automating the repetitive tasks involved in invoice processing, OCR technology frees up accounting staff to focus on more strategic and analytical activities.


This shift in focus enhances overall accounting efficiency, improves financial reporting accuracy, and supports better financial decision-making.


It also enables accounting departments to handle higher volumes of work without needing to expand their teams.


Improved vendor relationships


Faster and more accurate invoice processing enabled by OCR technology leads to timely payments to vendors.


This efficiency helps build and maintain strong vendor relationships, reduces disputes and payment delays, and fosters a positive reputation for the business in its supply chain network.


Timely payments also strengthen negotiating positions with vendors and may lead to better terms and discounts.


Increases scalability


OCR technology allows businesses to easily scale their accounts payable operations without significantly increasing resources.


Automated processing enables organizations to handle growing volumes of invoices more efficiently, supporting business growth and expansion without the need for proportional increases in headcount or infrastructure.


This scalability supports dynamic business environments and adjusts to varying transaction volumes seamlessly.


Allows remote access


With OCR technology, invoice processing can be managed remotely, providing flexibility for accounts payable teams to work from different locations.


This remote access capability is especially beneficial in today’s hybrid and remote work environments, ensuring continuity and efficiency in financial operations regardless of location.


It also supports disaster recovery and business continuity plans by decentralizing data access and management.


Simplified auditing and compliance


OCR technology simplifies the auditing process by providing easy access to digital records and accurate financial data.


Automated data extraction and validation ensure compliance with regulatory requirements, reducing the risk of errors and discrepancies during audits and enhancing overall financial transparency.


This streamlining of the audit process also shortens audit timelines and reduces associated costs.

What are the limitations of automated invoice scanning?


While automated invoice scanning with OCR technology offers numerous benefits, it is not without its limitations.


Businesses must be aware of these limitations to effectively implement and optimize OCR solutions in their accounts payable processes, such as dealing with varying invoice formats and ensuring high accuracy in data extraction.


Incomplete removal of manual verification


Even with advanced OCR technology, some level of manual verification is often required to ensure data accuracy.


OCR software may struggle with poorly scanned documents, unclear text, or non-standard invoice formats, necessitating manual review and correction.


This need for manual intervention limits the full automation potential of invoice processing and can impact efficiency gains.


Furthermore, the ongoing need for manual oversight can create bottlenecks, counteracting the benefits of automation.


Varying and complex invoice formats


Invoices come in various formats, layouts, and designs, which can pose challenges for OCR software.


Non-standard or complex invoice formats may result in incorrect data extraction or require custom templates and rules, increasing the complexity and cost of implementation.


OCR solutions need continuous updates and adjustments to handle diverse invoice formats accurately.


Adapting OCR systems to accommodate new invoice formats can be resource-intensive and require ongoing maintenance.


Accuracy issues with handwritten texts


Handwritten invoices or notes on invoices present a significant challenge for OCR technology.


While modern OCR solutions have improved in recognizing handwritten text, their accuracy is still lower than for printed text.


Handwriting recognition often requires manual verification, reducing the overall efficiency of automated invoice processing.


Additionally, the variability in handwriting styles can make it difficult for OCR systems to achieve consistent recognition, leading to potential errors in data capture.


Complexity in implementation


Implementing OCR technology for invoice processing can be complex, requiring significant investment in time, resources, and training.


Businesses need to integrate OCR solutions with existing accounts payable and ERP systems, customize templates and rules, and ensure user training and support.


This complexity can deter smaller businesses from adopting OCR technology.


Moreover, the initial setup and integration phases can be time-consuming, potentially disrupting existing workflows during the transition period.


Limited contextual understanding of the task


OCR software lacks the contextual understanding required to interpret the content of invoices fully.


While it can accurately extract text and data, it may not understand the context or meaning behind the information, leading to misinterpretations.


This limitation requires additional validation and review processes to ensure accurate data capture.


As a result, businesses may need to implement supplementary algorithms or human review processes to interpret and validate the extracted information.


Over-reliance on scanned image quality


OCR technology relies on high-quality scans for accurate data extraction.


Poorly scanned documents, low-resolution images, or documents with excessive noise can reduce OCR accuracy and require manual intervention.


Ensuring high-quality scans can add to the operational complexity and costs of OCR solutions, particularly for businesses with a high volume of invoices.


Additionally, the need for consistent scanning quality across diverse document sources can be challenging to maintain.


Handling of non-text elements


OCR technology is primarily designed to recognize text and may struggle with non-text elements such as logos, stamps, special symbols, or signatures.


These elements can interfere with data extraction, leading to errors or incomplete data capture.


Businesses need to implement additional processes or tools to handle non-text elements effectively.


This can also necessitate additional customization and validation steps to ensure that non-text elements are appropriately addressed in the data extraction process.

How does machine learning improve OCR-based invoice processing?


Machine learning significantly enhances OCR-based invoice processing by improving the accuracy and efficiency of data extraction from invoices.


By leveraging advanced algorithms and continuous learning capabilities, machine learning models can recognize complex patterns, improve character recognition, and automate various aspects of the invoice processing workflow, reducing manual intervention and errors.


Continuous improvement


Machine learning models continuously learn from new data and user feedback, leading to ongoing improvements in OCR accuracy and performance.


As the system processes more invoices, it identifies patterns and refines its algorithms, reducing errors over time.


This adaptability enables the OCR technology to handle evolving document formats and maintain high accuracy levels in invoice processing.


Reduction of errors


Machine learning enhances OCR’s ability to detect and correct errors in invoice data extraction.


By comparing extracted data against expected patterns and historical records, the technology identifies discrepancies and flags potential errors for review.


This proactive error reduction improves data quality and minimizes the need for manual corrections, streamlining the accounts payable workflow.


Contextual extraction of line-item data


Machine learning enables OCR software to perform contextual extraction of line-item data from invoices, accurately capturing details like item descriptions, quantities, and prices.


By understanding the context within the invoice layout, machine learning models can extract complex data fields that traditional OCR systems might miss, ensuring comprehensive and accurate data capture for accounts payable processes.


Automated invoice routing to approver


Machine learning improves the OCR invoice processing workflow by automating the routing of invoices to the appropriate approvers based on predefined rules and historical patterns.


This automation reduces delays in the approval process, accelerates invoice payment cycles, and enhances overall accounts payable efficiency, ensuring timely payments and maintaining strong vendor relationships.


Improved character recognition


Machine learning algorithms enhance OCR’s ability to recognize characters accurately, even in varying fonts, sizes, and orientations.


Through training on large datasets, these algorithms learn to identify and differentiate between similar characters and symbols, reducing recognition errors.


This improvement is very important for accurately extracting data from invoices with diverse formats and styles, which ensures consistent and reliable data capture.


Integrates with accounts payable systems


Machine learning-based OCR solutions seamlessly integrate with accounts payable systems, automating the end-to-end invoice processing workflow.


By connecting with existing ERP and financial software, OCR technology automates data entry, validation, and approval processes, reducing manual effort and speeding up invoice processing times.


This integration improves overall efficiency and supports timely payments to vendors.


Enhanced pattern recognition


Machine learning algorithms excel at recognizing patterns in invoice data, such as recurring vendors, invoice formats, and line-item structures.


This enhanced pattern recognition allows OCR software to automatically categorize and process invoices based on predefined rules, improving processing speed and accuracy.


Machine learning improves invoice processing workflows by learning from historical data.


Automated GL code matching


Machine learning enhances OCR’s ability to automate the matching of invoice data to general ledger codes, streamlining the accounting process.


By learning from past transactions, machine learning models predict the appropriate ledger codes for new invoices, reducing manual data entry and ensuring accurate financial records.


This automation speeds up the account reconciliation and improves reporting accuracy.

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Combining OCR with manual review


While OCR and machine learning technologies significantly improve invoice processing, they are not entirely foolproof. Certain complex invoices, poor-quality scans, or unique formats may still require manual review to ensure accuracy.


Manual intervention helps verify extracted data, handle exceptions, and provide feedback to refine OCR models. This approach allows businesses to catch errors that automated systems might miss.


Combining OCR with manual review ensures a more accurate and efficient invoice processing system, particularly when dealing with diverse or non-standard invoices.


Manual review and corrections


Manual review is essential for verifying data extracted by OCR software, especially for complex or unclear invoices.


Human reviewers check for accuracy, correct any misidentified data, and ensure compliance with company policies.


This process is crucial for maintaining data integrity, as OCR software may struggle with certain formats or handwritten notes.


Combining manual review with automated processing ensures high accuracy in accounts payable operations.


Exception handling


OCR software may occasionally flag exceptions, such as missing or ambiguous data fields, that require human intervention.


A manual review of these exceptions ensures accurate resolution and prevents processing delays.


Exception handling allows for the correction of errors and the clarification of any discrepancies, maintaining the overall accuracy and efficiency of the invoice processing workflow.


By addressing these flagged issues promptly, businesses can avoid costly errors and ensure smooth operations.


Feedback loop for OCR improvement


Manual review provides valuable feedback to improve OCR accuracy over time.


By analyzing the errors and adjustments made during manual reviews, machine learning models can be retrained to recognize similar patterns in the future.


This feedback loop enhances the OCR system's ability to handle diverse invoice formats and reduces the frequency of errors, leading to continuous improvement in data extraction accuracy.


Shifting focus on complex cases


Manual review allows accounts payable teams to focus on more complex invoices that require scrutiny.


While OCR handles routine invoices, human reviewers can concentrate on invoices with unusual terms, conditions, or formats that may not be easily processed by automation alone.


This division of labor ensures that all invoices, regardless of complexity, are accurately processed and that high-value tasks receive appropriate attention.


Manual review of flagged invoices


Invoices flagged by OCR for potential errors or discrepancies undergo manual review to ensure accuracy.


This process involves verifying data against original documents, checking for compliance with company policies, and ensuring that any anomalies are addressed.


Additionally, it helps identify patterns of recurring errors. Manual review of flagged invoices is crucial for maintaining the integrity of financial records and preventing costly errors in accounts payable.


Suggestions for businesses


Businesses should consider choosing accounts payable automation service providers that offer managed services for manual reviews.


These providers combine OCR and machine learning technologies with expert human oversight to ensure accurate and efficient invoice processing.


By leveraging managed services, businesses can fully automate their invoice processing workflows while maintaining high data accuracy and compliance, reducing operational costs, and enhancing overall financial management.

Best practices for implementing OCR in accounts payable


Implementing OCR technology in accounts payable processes requires careful planning and consideration to maximize its benefits.


By following best practices, businesses can ensure a smooth integration of OCR solutions, improve data accuracy, and enhance overall efficiency,  as well as streamline the management of exceptions and anomalies in invoice data.


Determine your business needs and goals


Before implementing OCR, it is essential to assess your business needs and goals. Identify the specific pain points in your accounts payable process and determine how OCR technology can address them.


Consider factors such as the volume of invoices processed and the diversity of invoice formats.


Establish clear objectives, such as reducing manual data entry, improving accuracy, or accelerating invoice processing times, to guide the implementation strategy and measure success.


Evaluate OCR solutions


Choosing the right OCR solution is critical to the success of your implementation.


You should evaluate different OCR software based on features, accuracy, ease of integration, and scalability.


Consider whether a cloud-based or on-premise solution is more suitable for your business needs.


Engage with vendors, request demos, and conduct pilot tests to assess the performance and suitability of the OCR solution.


Standardize document formats and layouts


Standardizing document formats and layouts can enhance OCR accuracy and efficiency.


Work with your vendors to adopt standardized invoice formats that are easier for OCR software to process.


Clearly defined templates help minimize errors and reduce the need for manual corrections, ensuring smoother integration with existing systems.


Encourage consistent document practices to optimize the OCR process and improve data extraction quality, ultimately reducing processing time and costs.


Ensure high data quality during pre-processing


Data quality is crucial for successful OCR processing. Ensure that scanned invoices are of high quality, with clear text and minimal noise or distortions.


Implement pre-processing steps such as image enhancement, noise reduction, and skew correction to improve the quality of scanned documents.


Additionally, consider using software tools that automatically detect and correct common scanning errors. High-quality data input is essential for accurate OCR output and efficient invoice processing.


Integration with existing systems


Integration of OCR technology with existing AP and ERP systems is vital for optimizing workflow efficiency.


Ensure that the OCR solution can integrate with your financial software to automate data entry, validation, and approval processes.


Evaluate the compatibility of the OCR system with various file formats and data types used in your organization.


Proper integration streamlines invoice processing, reduces manual effort, and ensures consistent and accurate data flow across systems.


Customize and train the system


Customizing and training the OCR system to recognize specific invoice formats and terminologies used by your business can improve accuracy.


Work with the OCR vendor to configure the system according to your requirements and provide training data to enhance the model's learning.


Regularly update the system with new data to adapt to changing document formats and maintain high accuracy levels.


User training and support


Provide comprehensive training for your accounts payable team on how to use the OCR system effectively.


Ensure that users understand the system's capabilities, limitations, and best practices for achieving optimal results.


Include hands-on training sessions and provide user manuals or digital resources for reference.


Ongoing support and training are essential for addressing any challenges and ensuring that the team can effectively utilize the OCR solution.


Look for managed services for manual reviews


Consider choosing OCR solutions that offer managed services for manual reviews.


Managed services provide human oversight to verify data accuracy, handle exceptions, and provide feedback for continuous improvement. This support is particularly valuable for organizations dealing with a high volume of complex invoices.


This approach ensures high data accuracy and compliance, particularly for complex or non-standard invoices that may not be fully processed by automation alone.


Continuous improvement


Implementing OCR technology is not a one-time process; it requires continuous improvement to maintain efficiency and accuracy.


Regularly review OCR performance, monitor error rates, and gather feedback from users to identify areas for improvement.


Encourage collaboration between IT and accounts payable teams to address system issues promptly.


Update and refine the OCR system as needed to accommodate new document formats, enhance accuracy, and optimize overall accounts payable operations.

Automate your invoice processing with Volopay’s Magic Scan!


Volopay’s Magic Scan is an advanced solution designed to revolutionize your invoice processing. This cutting-edge accounts payable software uses sophisticated OCR technology to automatically capture and process invoice data, transforming your invoicing workflow into a highly efficient and streamlined operation.


Magic Scan extracts essential information from invoices, including vendor details, invoice numbers, and line-item data, with exceptional accuracy. The software's advanced algorithms continuously learn from past data, improving accuracy and reducing errors over time.


The software integrates seamlessly with existing accounting systems, ensuring a smooth transition from invoice receipt to payment. By automating the data extraction and validation process, Volopay’s Magic Scan significantly reduces the need for manual data entry, minimizing errors and speeding up processing times. This efficiency translates into faster invoice approvals, timely payments, and enhanced vendor relationships. 


In addition to its robust data extraction capabilities, Magic Scan provides strong security features to protect sensitive financial information and comply with data protection regulations. The software includes encryption and secure access controls to safeguard data against unauthorized access.


With Volopay’s Magic Scan, businesses can achieve a fully automated accounts payable process, freeing up valuable resources, reducing operational costs, and focusing more on strategic financial management.

Seamlessly integrate Volopay’s Magic Scan into your business

FAQs

What does OCR mean on an invoice?

On an invoice, OCR refers to the technology used to scan and convert invoice data into digital text. This allows for automated data extraction, such as invoice numbers and amounts, which streamlines the accounts payable process and reduces manual entry.

What are some examples of optical character recognition?

Examples of optical character recognition include digitizing scanned documents, extracting text from images, and converting printed forms into editable formats. OCR is used in applications like document management systems, automated data entry, and digital archiving.

Is OCR accurate in extracting data from accounts payable documents?

OCR is generally accurate in extracting data from accounts payable documents, though accuracy can vary based on document quality and system sophistication. Advanced OCR solutions with machine learning improve accuracy over time by learning from past data and user corrections.

Related pages to accounts payable

Explore our guide on 2-way matching to understand how to effectively verify invoices against purchase orders, ensuring efficient payment processing.

Discover our guide on 3-way matching to learn how to reconcile invoices, purchase orders, and receiving reports, enhancing accuracy and control.

Read our guide on 4-way matching for a thorough approach to validating invoices, purchase orders, receiving reports, and contracts terms.