Invoice data capture software is a technology designed to extract relevant data points from invoices. Common examples of these data points include:
Optical Character Recognition (OCR) data extraction technology converts unstructured data from a scan or PDF of an invoice into searchable, machine-readable text. For example, if you input a batch of invoices, OCR data capture software would release a structured file containing VAT totals ready for downstream processing. OCR invoice capture software can also extract handwriting – such as signatures or notes on an invoice – and convert it into raw text.
In the 1920s and ‘30s, Emmanuel Goldberg developed an early version of OCR that used an optical recognition system to trawl microfilm archives. While OCR was the first data capture technology, is it still the best out there?
For enterprises, OCR for invoices will likely lead to a disappointing experience. Invoices are semi-structured documents - while they conform to a general structure, there are variations and multiple optional elements. There is no such thing as a typical invoice, meaning it is extremely difficult to train an OCR engine to locate the pertinent data points across all invoices. In general, OCR’s most effective use cases rely on clearly defined templates, such as number plates and cheques.
Ultimately, does OCR-based invoice capture software work satisfactorily for invoices? The answer is not really – at least, not without a significant degree of training and correction. In the interest of fairness, however, you will likely experience success with OCR if you’re working with a high volume of invoices with completely static layouts.
One of our founders, Dr. Martin Goodson, described his experience working with traditional OCR technology:
“We founded a startup for technology to automate tax calculations for self-assessment to make tax returns easier. The OCR technology we used wouldn’t read usage slips and kept breaking. We were shocked – previously, we assumed that OCR just worked. I had no idea it was so primitive. So the startup failed.”
Motivated by the frustration that plagued their previous project, our founders created Evolution AI. Many other tech entrepreneurs have faced similar experiences, creating a range of alternatives to OCR currently on the market. As for the best alternatives to OCR available commercially? AI-based data extraction is one of the strongest contenders out there.
AI-based data extraction uses OCR technology as its foundation, with extra elements integrated to enhance its functionality, such as natural language processing technology (NLP). NLP technology allows intelligent data extraction technology to actively understand the data’s meaning.
With invoices, for example, understanding the meaning of the text is critical because so many terms share a similar meaning. Take terms such as ‘invoice number’ and ‘order number’ or ‘invoice date’ and ‘invoice pay date’. The subtle lexical differences between each pair conceal significant semantic differences that non-intelligent technology like OCR would struggle to process. Ineffective extraction can then produce potentially disruptive (and costly) consequences downstream.
One distinct advantage OCR invoice capture software may have is its long history in enterprise settings. Because of this, companies looking to integrate this data capture technology into their workflow may experience a better reception from stakeholders.
Of course, it’s always better to determine what you’re expecting from a potential solution and whether your needs closely match the relative strengths of each technology you’re considering.
Regarding cost, both OCR and AI data capture solutions offer a spectrum of price tags. However, it’s the reliability of the technology that determines its overall value. If you deploy an inflexible and unreliable data capture solution – such as the one our founders used – your employees will waste considerable time and funds.
Another key difference between OCR and AI invoice data capture is that AI works on all documents, sometimes without any training documents (an AI concept known as zero-shot learning). Consequently, if your business’s use case changes or expands outside of invoices, you won’t need to re-calibrate the extraction tool.
Ultimately, it’s worth remembering that AI was developed as an alternative to OCR. Although OCR still has merit, you’ll want to take a measured look at the requirements of your data extraction project.
Evolution AI continues to automate data extraction from invoices for large enterprise clients, such as Novuna Business Finance and DF Capital. Our automated data extraction software for invoices delivers cost-effectiveness and scalability.
To speak with a member of our team about how AI-based data extraction could become a part of your business’s roadmap, book a demo or email us at hello@evolution.ai
