OCR for handwriting. Something once considered impossible to achieve, is now here. But what is it? How does it compare to traditional OCR? How does it actually work? There are countless questions. And since we get asked many of them every day, we decided it might be helpful to put all our answers in one place.
Handwriting OCR (optical character recognition) is the process of automatically extracting handwritten information from paper, scans and other low-quality digital documents. But while the definition of handwriting OCR is relatively straightforward, the process itself? Not so much. To really understand the impact of this technology, let’s take a look at the differences between traditional OCR – and the kind of OCR that can read handwriting.
Before handwriting OCR, there was traditional OCR. This is what helped shape the meaning behind the phrase “optical character recognition.”
Traditional OCR is all about technology that has “studied” fonts and symbols enough to be able to identify almost all variations of machine-printed text. But therein lies the limitations of traditional OCR: while it’s great for extracting text from paper, it can’t read handwriting. There is simply too much variety.
For a while, traditional OCR was all we had. So, organizations had to take a few shortcuts to make up for its limitations and get the work done.
Traditional OCR could handle the easy stuff – about 80 percent of document workflows. For the more complicated stuff (like handwriting), humans had to intervene and perform manual data entry. 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 years, organizations simply accepted that this was the extent of OCR’s capabilities. Handwriting recognition was impossible. Eventually, people stopped looking for it, resigning themselves to dealing with a two-tiered capture system.
Handwriting OCR achieves what traditional OCR never could. But getting there is a lot more involved than just creating “better software.” Here’s how it works:
Handwriting OCR requires much more advanced technology than traditional OCR. 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.
The combination of highly trained machine learning models and computer vision engines is what makes it possible for handwriting OCR to replicate the way humans read handwriting. In fact, if the model is good enough, it can read handwriting better than humans – but we’ll get to that.
Machine learning models are only as good as the dataset they’re trained on. This means the bigger the dataset, the better the training, the more effective the model.
But it’s not just the quantity of data, it’s the quality too. Training requires a lot of specific data, like new forms and workflows. Over time, the algorithm will improve as it continues to learn.
But the most important performance gains (such as 90 percent accuracy and above) are incredibly resource-intensive and require a serious amount of quality data. This is why we took the time to train our AI with 1 billion human-verified data points.
And then, of course, you need to put the model into practice. This requires a large dataset of what you want to digitize (usually different types of forms that you normally see in your processing workflow), experts to help you build out a model based on those forms and ongoing support to help you refine it over time.
So, yes – handwriting OCR exists. But who is using it – and who is making it all possible?
Handwriting OCR is great in theory, but what does it look like in practice? How are businesses deploying it and what kinds of results are they seeing? Let’s take a look.
Any business burdened with massive amounts of information arriving on paper and under constant pressure to “do more with less” can benefit from handwriting OCR. Paperwork processing – a necessary evil for many organizations – is one such example. Processing is common for insurance and healthcare organizations. It’s painful because it often steals away time and resources for manual data entry. Handwriting OCR allows them to reallocate. Here are a few more areas where it can help.
Beyond automating the processing workflows, handwriting OCR also provides a level of data access that produces better analytics and decision-making. Before this type of OCR, teams were just processing paper to get the job done. Now, they can process paper and make the job better.
Handwriting OCR can have direct and indirect benefits for an organization. And in both the short and long-term. Here are a few of the more typical ones.
Many handwriting OCR applications deliver amazing benefits. But it doesn’t happen overnight. The complex nature of setup and implementation means that, for some businesses, it could take years to get up and running with a model that actually delivers the goods. So, if you want handwriting OCR, how do you get it?
If your business or organization needs a handwriting OCR solution, do your homework. Not every provider does it the same way. Words like “AI” and “machine learning” are tossed around a lot. But few are able to back it up with explanations of how their technology works. Finally, when it comes to numbers around accuracy and performance, look for only the most transparent vendors.
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