Computer vision: another subset of artificial intelligence that can automate tasks that the human visual system can do.

Machine learning: a subset of artificial intelligence that provides systems with the ability to automatically learn and iterate from experience without explicit instructions, relying on patterns and inference instead.

Instead of using simple techniques to identify letter shapes, this type of OCR leverages a highly trained machine learning model and advanced computer vision engines to actually read what is written like a human would. Handwriting OCR requires much more advanced technology than traditional OCR. But getting there is a lot more involved than just creating “better software.” Here’s how it works: AI, Machine Learning & Computer Vision Engines Handwriting OCR achieves what traditional OCR never could. Eventually, people stopped looking for it, resigning themselves to dealing with a two-tiered capture system. Especially because the people using the technology were in tightly regulated industries with sensitive information, like financial services, government and healthcare organizations.įor years, organizations simply accepted that this was the extent of OCR’s capabilities.

Security: the transfer from machine to human back to machine created concern.

Resources: difficult to source talent willing and able to manually extract handwriting.

Accuracy: mistyping and exception handling.

While 20 percent of manual data entry is better than 100 percent, this two-tiered capture system – OCR versus humans – was burdensome and created three major challenges: For the more complicated stuff (like handwriting), humans had to intervene and perform manual data entry. Traditional OCR could handle the easy stuff – about 80 percent of document workflows. So, organizations had to take a few shortcuts to make up for its limitations and get the work done. For a while, traditional OCR was all we had.

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