The practical applications of Deep Learning and its impact on document automation promises to accelerate change in business processes. Find out how here.
![Deep Learning: Practical Applications for Advanced Capture](https://www.parascript.com/wp-content/uploads/2020/03/deep-learning-practical-applications-730x376.jpg)
The practical applications of Deep Learning and its impact on document automation promises to accelerate change in business processes. Find out how here.
We are entering the end of the “Feature Wars” era for document automation, data capture and what that means to business processes.
For document automation self-learning software spells the end of vendor feature wars so that businesses can focus on the outcomes; find out why here.
Natural Language Processing or NLP helps make document automation highly successful for certain tasks and fails miserably in others that are explored here.
Take an in-depth look at document processing automation at the core of cognitive RPA or Robotic Process Automation and its impact on operations.
Tackling more complex processes leveraging enterprise Robotic Process Automation (RPA) requires overcoming some significant challenges. This article explores how to meet those challenges.
With all of the capture solutions available, how do you know which one is right for your enterprise? This article explores the top 5 questions to ask before buying a solution.
How to move beyond data extraction and OCR as core competencies in document processing in order to meet evolving client data needs and expectations.
Many capture systems cannot provide the level of certainty necessary for data extraction to be truly automated–find out why…
Advanced image processing for scanned documents is now more important than ever—find out why and how it makes your life easier in this article.
Often in conversations with clients and prospective clients, we hear phrases such as “we want to get 90 percent accuracy” from our system, but what does that really mean?
When it comes to the classification of documents, one accepted approach is using text. And yet, visual classification can offer a powerful alternative.