OCR is a technique that enables the conversion of static information, including physical forms, into an accessible and editable format. It can recognize a variety of text types, including typed fonts and handwritten letters. Many businesses see optical character recognition (OCR) as a critical tool for automating their procedures. OCR technology is a hardware/software combination that scans a paper document, typically an invoice, and converts it to metadata that may be used to update fields in a database. The invoice can then be integrated into an electronic workflow.
In the field of document management, OCR (optical character recognition) has become ubiquitous. Accuracy has increased to around 90%, putting it on par with manually entered data (and often even more accurate). The productivity benefits are apparent, and the business case is self-evident, both in terms of actual cost savings (lower labor expenses) and all the intangible benefits associated with reduced errors, shorter cycle times, and happier employees and suppliers. Many payments are now processed electronically in today's developing work environment. Large organizations are becoming aware that handling paper invoices are more expensive than processing computerized ones. In general, many businesses are incorporating OCR technology into their operations.
Each year, businesses invest millions of dollars in off-the-shelf OCR software intending to improve how they process mountains of paper invoices. However, mid-market organizations that have adopted OCR as a strategy are discovering that it is ineffective. It's merely a Band-Aid applied to a far more severe problem. To be strategic, you must think beyond simply improving how paper bills are processed. It would be best to consider a holistic strategy for processing the invoice more efficiently.
OCR Management of Vendor Invoices
Vendor Invoice Management Systems are not new for accounts payable operations. On the market, there are a plethora of OCR-based invoice management solutions. Some of these solutions are more efficient in workflow, while others offer superior OCR capabilities. However, businesses no longer want generic answers in the age of online apps. Let us attempt to comprehend what clients desire in new-age software and, more specifically, in the area of invoice management.
· Accuracy of Data Entry:
Even the most sophisticated automated OCR systems have the potential to mislead your invoices and enter inaccurate data into your system. Incorrect data results in increased exception handling and further review by your team to rectify it. Time spent resolving exceptions and reviewing errors contradicts the purpose of acquiring the OCR technology in the first place. A robust OCR tool ensures that all invoice data entering your financial system is accurate and minimizes time lost due to erroneous data. This will assist your team in establishing a touchless procedure that maximizes your AP process's cost and time efficiency.
· Complete data:
Data is crucial – and ensuring that the data is accurate is crucial to creating a flawless AP process. Another issue is that most invoice OCR technologies only take data down to the invoice header level, so you're not collecting all the precise purchase information you need to make the most informed spending decisions. It is vital to retrieve invoice data down to the line level to get a complete view of where your money is going. Without comprehensive invoice data, you will be unable to answer queries such as the following:
i. What exactly are we purchasing?
ii. What are we purchasing?
iii. Are we purchasing from the appropriate vendor?
iv. Are we on a budget?
v. Where can we improve our efficiency in terms of cost savings?
· Saves money:
The retail store franchise saves money by automating invoice digitization with OCR and deep learning. An invoice that must pass through three reviewers to ensure there are no errors is reduced to one. A computer can process multiple times the number of invoices that a human can. This period includes determining whether the invoice is fraudulent, ensuring that it contains all the necessary information, verifying that all of the information is correct, manually entering all of the data into a spreadsheet or database, performing calculations, and finally handling the payment.
· Better Invoice management:
Despite the world becoming increasingly digital, some businesses continue to deliver paper invoices. OCR technology improves invoice administration by allowing office personnel to import data from physical invoices by allowing the OCR application to recognize and convert the text on the page to a digital version. Then, those who deal with invoices will have all the relevant information in one location and will not have to worry about misplacing documentation. Additionally, because OCR technology enables keyword searches inside digitized content, individuals can save time checking payment-related details, such as what a client purchased and when the transaction occurred.
· High efficiency:
Integrating OCR into your invoice automation solution might result in significant efficiency and cost savings. These benefits eliminate substantial amounts of manual searching and help minimize errors that could occur otherwise, such as when people manually enter data and make mistakes.
How it Operates
• Understand how your OCR technology extracts data and contributes to and fits into your organization's invoice validation process. Not all invoice images are made equal, and retrieving line item data from a 300-page phone bill is just as pointless as failing to extract line item data from a purchase order invoice.
• To protect your interest in the future, the OCR system's data extraction process should not be dependent on "template"-driven algorithms. The solution should read the entire invoice image and accurately decode and map the data to the appropriate fields within your organization. Vendor invoice formats frequently change, and an inflexible solution may result in your (lack of) quality is associated with an out-of-date design.
• To guarantee the accuracy, the OCR solution's output should be compared to your data. Of course, this does not include all fields but rather key ones such as vendors, vendor addresses, individual names, and purchase orders. The data in the image should not be taken at face value; it should be matched to and validated against the data in your systems.