Tired of your document processing software making loads of mistakes?
You're not alone.
Most AI document processing systems struggle with the real world. They can't handle all the variations in documents you throw at them, and their accuracy can be...well, questionable.
This blog post will reveal the 2 dirty secrets of AI document processing and show you how to get a system that actually works for you. Learn how to:
Get ready to transform your document processing from frustrating to fantastic...
I'll let you in on a little secret: We rarely know all the variations.
But the worst part is, most clients don't realize how much variation they have in their documents until they start a document processing automation project. It's just part of the process.
I jokingly call this "getting intimate with the data." But this is an important step and is what's needed while the AI document processing system is being built.
So what does this mean? Look for a software vendor that can design a system that will perform exceptionally well in production from that good, clean source data.
It doesn't sound all that technologically advanced. Still, the reality is that if a new variation comes along and doesn't get good recognition, the AI document processing system must be adjusted by a human to accommodate that new variation.
In my opinion, AI is getting worse right now.
The focus on GPT engines (Generative Pre-trained Transformers) has introduced us to the ugly side of AI: false data that looks correct.
If you’ve recently seen an online article that had blatantly incorrect factual data, the website was probably generated by the newest generation of GPT engines. They are adept at making text that looks like how humans write.
The problem is, and has always been, the AI doesn’t understand what it’s doing: it’s a talking parrot, unable to understand what it is creating.
Even these fancy, state-of-the-art engines, which are creating bad data as well as good data, have been human-trained.
Some intelligent document processing (IDP) solutions, like Grooper, have a lot of tools to help you facilitate a timely resolution to adding in new variations of documents. For instance, with these top solutions:
Easy peasy, as they say.
So yes, AI document processing software does get smarter with time. But not on their own.
Sometimes optical character recognition (OCR) is 100% confident, and the results are still flat wrong. (If your system doesn't have a way to fix poorly OCR'd data, you need to talk to us!)
The top-of-the-line automated document processing software, like Grooper, can force the data into the format it's looking for. How?
By using natural language processing concepts that are built into an implementation of Regular Expressions (REGEX). Once you turn on "fuzzy" regex, you now have the ability to force data into the form you want it to be in.
For example, here's how I used NLP and regex in electronic document processing to correctly extract medical ICD 10 codes:
If the Regular Expression engine wanted to make a swap, the resulting confidence would be below our threshold of 99%, so no match would result.
But with Grooper and a weighting, I can guarantee that the swap will happen if OCR commits an error in its results.
It's all about getting good data from bad, and it's an essential part of top-of-the-line AI / cognitive document processing solutions like Grooper.
We can massage data for you at scale to allow you to fully benefit from AI-based document processing. So what does this mean? Fewer errors directly relates to less cost of ownership and higher instances of straight-through processing.
Grooper was built to succeed where other IDP systems fail. So give us a call, and we'd be happy to show you how Grooper can help process documents.
Other benefits include:
Improve your organization's efficiency by extracting data from structured, semi-structured or unstructured documents and making that structured data available to your line-of-business solutions and users.
Automate and validate all your intelligent documents extraction to streamline compliance workflows, drastically reduce manual entry, reduce guesswork, and keep data accurate and compliant.
With AI document processing, you can leverage new insights from your data to meet customer demands and improve satisfaction, advocacy, lifetime value, and spending.
Reading and understanding documents is a challenging task for technology as there is a wide variety of document structures and formats, and poorly scanned document images.
Intelligent Document Processing can significantly enhance business operations, especially when it works with artificial intelligence (AI). Machine Learning (ML) and AI technologies like natural language processing (NLP) offer huge benefits to organizations and are crucial components of digital transformation efforts.
Document processing with AI involves five separate stages: document ingestion, condition, classification, extraction, and delivery.
One of the primary use cases for these technologies is AI document processing, which automates document recognition and extraction of critical information.
That's because the business world generates massive volumes of documents, such as invoices, forms, contracts, and receipts. The sheer volume of paperwork can cause numerous issues, including processing delays, errors, and significant time spent on manual data entry.
However, AI document processing can automatically recognize various document types and extract relevant data without manual intervention. By streamlining the document processing workflow, IDP and AI can boost efficiency, reduce errors, and free up valuable resources to focus on more important tasks.
This is a step-by-step guide to AI document processing:
The first step in IDP is to import the document into a software that uses document AI features. This can be done by:
This stage prepares documents for further processing by normalizing the format and cleaning up document images. IDP then uses full-text OCR to recognize data in the form of text and images in the document.
The next step is for the system to classify the document type. Basically, the software needs to understand the document layout and use document AI to determine whether it’s an invoice, purchase order, receiving document, EOB, contract, mortgage document, or financial form.
Once the software recognizes the type of document, it can begin to process it.
The AI then extracts data from the document and converts it into a format that a machine or person can use. This involves translating the unstructured data in the document into structured data that software can easily work with. The IDP system also sends data to humans for reviewing if there is an issue with extracted data.
For example, the IDP could read an invoice, send data into ECM or downstream systems, facilitate two or three-way invoice matching, and then notify the accounts payable department if there is a discrepancy in a new invoice.
Finally, the IDP system exports documents and the data within to a pre-defined destination.