This week is a deep-dive into the second factor, which addresses how modern IDP software can learn and improve—different solutions tackle the problem in different ways—some better than others.
Modern IDP software enables the automation of processes due to key factors including the ability to work in suboptimal conditions discussed here.
In automation, using that most derided approach—the template—can be useful, but it has its limitations with the best and worst discussed here.
Explore if and when templates are useful in Intelligent Capture and how machine learning is used for highly variable documents or unstructured documents.
How to configure your Intelligent Document Processing (IDP) software so it does what you want it to by leveraging truly intelligent capture.
Is there such a thing as machine learning that does NOT require training on sample data? The answer is “sort of” – find out why here.
Every organization with complex processes can benefit from automation by automating manual processes to reduce effort, but how do you do that for core processes?
Explore what Natural Language Processing (NLP) means and when you really need NLP to automate document-oriented processes. This article also delves into document classification – what it does and why organizations use it.
Find out about Intelligent Document Processing (IDP) strategies and shortcuts for what amount of data is satisfactory and how to get there.
There’s no easy button for intelligent document processing, but we are getting closer with the use of deep learning. Find out how.
Key developments in handwriting recognition are discussed here. This includes handwriting recognition innovations and their importance today.
Acquiring automation solutions often means conducting a Proof of Concept or evaluation because technology solutions are often too complex to assess simply by comparing a list of features. Find out how to conduct a successful PoC here.