Handwriting OCR

Can AI-powered OCR really read handwriting better than a human?

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. 

What is Handwriting OCR?

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.

Traditional OCR

Before handwriting OCR, there was traditional OCR. This is what helped shape the meaning behind the phrase “optical character recognition.”

  • Optical – relating to sight
  • Character – a printed or written symbol, letter or number
  • Recognition – identifying something from previous encounters

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.

The Challenges of Traditional OCR

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:

  • Accuracy: mistyping and exception handling
  • Resources: difficult to source talent willing and able to manually extract handwriting
  • Security: the transfer from machine to human back to machine created concern. Especially because the people using the technology were in tightly regulated industries with sensitive information, like financial services, government and healthcare organizations.

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.

How Does Handwriting OCR Work?

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:

AI, Machine Learning & Computer Vision Engines

Handwriting-OCR-Sample-FormHandwriting 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.

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

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.

Training Machine Learning Models

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.

Putting Your AI Machine to Work

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 Applications & Benefits

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.

Who is Using Handwriting OCR and for What Purpose?

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.

How Does Handwriting OCR Benefit an Organization?

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.

  • Greater straight-through processing for automation
  • Reduced exception handling
  • Accomplish 80% of the work with 20% of the staff.

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?

What to Look for in a Handwriting OCR Solution

Handwriting-OCR-DigitizationIf 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.

Technology

  • Is their solution AI-powered or is it just a well-marketed, human-data-entry and machine hybrid?
  • Can they explain the math behind their solution? What kind of machine learning models do they employ?

Accuracy

  • How accurate are they? Can they provide a number (95%, 99%, etc.)?
  • Can they provide accuracy numbers for every process they perform and every document they read and extract?

Experience

  • Are they a fresh startup or have they been doing it for close to a decade (like us)?
  • Why are they in the OCR game? Vidado got started doing crowd data entry. This is what gave us the largest human-verified dataset (1 billion+ fields) in the industry.

Ease of use

  • Do they offer a cloud-based SaaS solution, or must you host it onsite?
  • How soon before you can start using the product? Many providers take about 6 months to a year to achieve high-level accuracy. (Vidado offers it on Day 1.)
  • How much training is required? If it’s an AI-powered platform, will you need machine learning expertise on staff? Or will the provider handle everything (like we do)?

Business case

  • Do they have experience solving real business issues with their technology?
  • Have they imbued their technology with lessons learned from that experience? We have.

Try Handwriting OCR for FREE

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