optical character recognition

What if the OCR misses a field or value?

What if the OCR misses a field or value?

You have a software package that relies on optical character recognition (OCR) to classify, pick up words, numbers or phrases from a document.  As long as the quality of the document is mostly clean, everything works well.  However, what happens when the document arrives and the quality is simply, not good?  Does the software give up and run away with its tail between its legs? Are there any options to classify or capture anything on these documents?  

Top 3 Optical Character Recognition (OCR) Misconceptions

Top 3 Optical Character Recognition (OCR) Misconceptions

Optical Character Recognition can be an extremely powerful tool, but there are many things that an OCR engine can’t actually handle, that often times get overlooked. Below I have listed out the top 3 most common misconceptions of an OCR engine.

Leverage OCR to improve your workflows

Leverage OCR to improve your workflows

It’s easy to mistake Optical Character Recognition (OCR) as a one-trick pony. 

After all, pulling text out of an image to make it usable in other applications is an impressive trick.  Don’t be content to think that’s all OCR can do for you though.  By combining OCR output with other technologies, it’s possible to make substantial improvements to workflows throughout an organization.  Incoming document workflows are the first and most obvious place that OCR can make a major impact.

The most important scanning feature you never knew you needed: Optical Character Recognition

The most important scanning feature you never knew you needed: Optical Character Recognition

Discover how optical character recognition (OCR) software turns paper documents into digital files, simplifies data entry and searches.