That's a six.
That's an R.
That's an H.
And if you didn't know any better, you might think I'm getting an eye exam.
But I'm actually demonstrating my own combination of pattern recognition and a feature recognition in performing a little optical character recognition more simply known as
OCR.
Fortunately, this isn't something we really need to do the hard way anymore.
But before OCR it was fairly common for a person to sit there manually typing out the contents of page after page after page.
Look, some of the earliest work in OCR was pioneered by Ray Kurzweil.
yes--
That Ray Kurzweil, who develops technology in the early 1970s, capable of recognizing printed text in virtually any font.
From there, Ray
and his team developed a speech synthesis technology capable of reading printed text out loud.
So the next time your GPS lets you know, there's a left turn coming up.
Make sure to say thanks to Kurzweil Computer Products, Inc..
OCR has come a long way since then in both speed and accuracy and the ability to automate complex documents
processing workflows means formatted information can retain its structure after being scanned.
And as you can imagine, that's a huge benefit for industries dealing with forms and printed documents.
But how does it work?
Well, before we get down to decoding this and decoding that well, let's talk about how an OCR program first needs to analyze the structure of the document image.
It needs to do things like identify the area of text.
It needs to do things like figure out the lines of text, the spacing between the words and all sorts of other document elements.
And once it's loaded in the characters, they're rendered to a high contrast thing called a bitmap.
And from there they can be processed by any number of algorithms.
Speaking of which, the most common algorithm is known as
Pattern Recognition.
That's what I was doing right at the start.
Now pattern recognition involves first training a computer with a very large set of known characters.
Just like imagine a PowerPoint.
That's just like 8 million slides of the letter L all different possible representations of it.
Keep that in mind
Next time you're about to complain about a boring status call. With a learned understanding of what pretty much any imaginable variation of every character might look like.
It's just a matter of comparing the identified character and then finding the closest matching one.
Another common algorithm is known as feature analysis. And feature analysis
is a little bit different.
From pattern recognition.
It relies on the characteristics of each individual character, like how many lines it has, whether it has curved lines, if any of those lines intersect.
So let's say that it sees two straight diagonal lines, something like.
These guys here.
So if it seems that they come together at the top, there's a high probability here that we're looking at either a letter A or a letter W, so it will check to see if there's a line connecting the diagonal lines.
Looks like an A or two more lines connecting to those first two lines at the bottom.
I can recognize a W. So where pattern analysis relies on lots and lots of examples to train a model, a big boring PowerPoint.
This is more rule based and it requires a deeper understanding of those characters on the part of the developer.
But in theory, it should be able to handle new fonts without needing to be retrained.
Suffice to say, OCR continues to be enhanced year after year.
Some early OCR needed to be manually guided and corrected, sometimes performing only slightly faster than a person at a keyboard.
But today's OCR can find and read a license plate, even when it's traveling on a vehicle under a toll bridge like 65 miles per hour, perhaps even faster.
OCR, combined with AI, has proved to be a winning combination.
It's what helps tell us Os from our zeros.
It tells us our AIs from our ALs.
It helps us distinguish our LOLs from our 101sl
By analyzing broader contextual and linguistic patterns, A.I.
is able to correct some mistakes that may slip through the cracks from OCR
perform that a purely character by character level. And don't just think books and forms.
The need to turn printed characters into ASCII characters will only accelerate. The traveler using an augmented reality app overseas to understand store signs.
The passengers in a self-driving car -- that'll be reliant on OCR and AI's ability to handle letters from things like dark, blurry video, confusing perspectives with light snow, faded paint, one sign in front of another where we're about to see this technology taken in some amazing new directions.
And all it asks in return is that we stop using Comic Sans.
It's seen every font in the entire universe trillions of times over, and it says - That's the worst one.
And the sooner we take care of that, the sooner we can get those self-driving cars.
Seems like a pretty fair trade to me.
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Thanks for watching.