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Accounts payable (AP) has evolved in the past few years. From manually entering invoice data into the company’s internal systems to reviewing the spreadsheets and sending it for approval, the process is now automated. Technologies like AI and machine learning have evolved invoice OCR AKA invoice data extraction from being human dependent to machine dependent. A series of changes and revolutions took place to mark this change in the data extraction process, however, the presence of OCR technology is still a topic of debate for many industry leaders.
Whether or not an OCR is relevant today or how accurate is invoice OCR data extraction or invoice OCR is a matter of concern. There are certain myths surrounding this quite old yet effective technology – optical character recognition (OCR).
Hence, we took the opportunity to dig in and put forward the reality of invoice data extraction using OCR.
4 myths surrounding invoice OCR data extraction
Myth #1 – OCR is a fully automated invoice data extraction software
The first myth is common for those who are new to the world of automation and the specifics of OCR technology. OCR is basically a software that converts scanned images into searchable text.
While it does convert images into digitized text, the resultant data may or may not be directly useful for processing, hence making the technology vulnerable to mistakes. Traditional OCR software is not designed to assign meaning to the text it extracts from the image you supply. It can merely dump the entire text found in that image. Hence, in the case of invoices also, it is impossible for traditional OCRs to accurately extract invoice field data into spreadsheets without human involvement for interpretation, verification and update.
Myth #2 – Template-based OCRs eliminate human intervention
Automated invoice processing or simply invoice processing requires structured data so the invoices can be verified and approved for payment. Also, the AP team could easily analyze the invoice data for gaining insights into the company’s performance and financial planning.
In an attempt to embrace automation and help the AP team focus on their core competencies rather than manual data entry, template-based OCRs replaced traditional OCRs.
However, instead of reducing human intervention, template-based OCRs increased human dependency for invoice OCR data extraction. The AP team got busy designing a new template for every invoice with a different layout/structure that entered the OCR. Consequently, there was more manual work involved than it was before.
Myth #3 – Invoice OCR data extraction is time-consuming and costly
OCR based data extraction is time consuming considerably only when the number of invoices is low. That is, it costs more time and money to set up an OCR software to process a small pile of invoices. However, bulk invoice processing can only be successful with an OCR in place.
OCRs extract data quickly from large amounts of images and OCR extracted data takes less time to process. Not to mention, Invoice OCR data extraction outperforms manual data entry in terms of speed. But it all depends on the quality of image entered in the OCR and storage capabilities of the system.
Also, the type of OCR being used to extract data from the invoices plays a major role in deciding the cost and timeline of the entire process.
Myth #4 – AI and ML uprooted OCR based data extraction
The concept of invoice process automation using AI made most of the business owners think that OCR is gone and it is no longer relevant in data extraction. However, the truth is OCR is the fundamental process behind data extraction and can never be replaced by AI and ML.
Artificial intelligence and machine learning, therefore, add to the already existing features of OCR to make it smarter and more productive for the businesses. These technologies fill up the loopholes in invoice OCR data extraction. AI and ML, natural language processing combine to impart intelligence and cognitive functioning to OCR so it can extract invoice data after understanding the context and interpreting the text per field.
OCRs are more relevant today than ever with the right integration of AI and ML algorithms that allow it to be more productive while also reducing human intervention to a great extent.
Myth – KlearStack is simply an OCR-based document scanning software!
KlearStack is much more than an invoice OCR data extraction software. It amalgamates the efficacies of AI, ML algorithms like deep learning, natural language processing, and RPA with OCR to be used as a template-less and end-to-end automated system.
Moreover, it supports document decision support, converting unstructured documents into actionable insights for the AP team. Residing in your cloud storage, KlearStack even makes itself more accessible for your team to work together on the same set of responsibilities.
If you want to know more about how KlearStack outperforms other software in terms of automated invoice processing, stay tuned for more updates or book a free consultation call with us today!
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