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.
Enterprises created a work-from-anywhere environment and events went exclusively online so when we attended intelligent capture webinars, many of us had the same questions, answered here.
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.
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.
Ontology is the latest buzzword in the intelligent capture solution domain. Sounds cool. But what does it mean? Does it really change things for the better?
More than just the end user experience, ease of use leveraging machine learning unleashes document automation for every organization. Find out how.
These practical steps for a Proof of Concept (PoC) offer guidance on assessing the straight through processing (STP) potential of any intelligent capture system.