These practical steps for a Proof of Concept (PoC) offer guidance on assessing the straight through processing (STP) potential of any intelligent capture system.
Adopting intelligent capture is risky if you don’t have all the facts. Here is actionable advice for avoiding the pitfalls and ensuring project success.
What it takes to build a cognitive capture solution from scratch is important due to the hidden elements of staff skills and OCR performance. Find out more.
Does Your OCR Suffer From Low Confidence? Explore how to ensure that accurate data passes straight into business workflows without manual verification.
Intelligent capture technology behind achieving STP requires accuracy. This means examining the elements of accuracy & how to evaluate it, discussed here.
With Intelligent Capture, what gets us into trouble…is approaching document automation as an OCR problem instead of a data science problem. Find out why.
Cognitive classification is one important intelligent capture application, but it differs from other types of classification: find out how & why it matters.
One area of automation that has not done a good job of keeping-up is Intelligent Capture, find out why & how to really excel in straight through processing.
Why machine learning is not the only answer and how to find the best solution to meet your organization’s document automation needs. Find out more here.
This article delves into the key technologies involved in cognitive capture & what areas they support by looking at standard document capture workflows.
So what does 99% accuracy really mean? This article explores what 99% accuracy means for your document data extraction or document automation
Is Machine Learning all the same? Let’s delve into the most common machine learning techniques, explore how they are used and where.