You know, it is pretty easy to take words on your computer screen and put them on a physical sheet of paper. Just click print and boom, you will have your data on paper. But going in the opposite direction, scanning dead tree information into your PC, is actually quite a bit trickier.
I mean sure flatbed scanners are not all that difficult to operate, but many of them are basically just taking a picture of the document and saving it on to your PC, meaning not only it will probably not look very crisp due to file compression and little bit of dust in your scanner.
But you can not edit a clean copy of your document in your favourite word processor, because the scanner won’t recognize each individual character.
Fortunately, there are a number of devices out there, that enable Optical character recognition or OCR. Where each character on a page is scanned individually. So, your papers will be uploaded as actual text documents instead of messy JPEGs.
But, how exactly does that work and how one kind of optical scanner is better than another?
Well, because the whole concept of translating text into electronic signal is pretty broad, there have been lots of different implementations of OCR over the years. In fact, one of the earliest electric OCR devices “the OptoPhone” was invented all the way back in 1914.
This bizarre looking contraption relied on the special behaviour of selenium, which conducts electricity differently in light and darkness.
As it scanned the words on a page, the OptoPhone distinguished between the dark ink of text and lighter blank spaces, generating tones that correspond to different letters making it possible for blind people to read with some practice.
Later in 1931, a machine was developed that could convert printed text to Telegraph code. One of the first technologies to translate printed characters to electrical impulses rather than sounds.
But it wasn’t until the 1960s and 70s that OCR began to take a more familiar modern form with postal services using OCR to read addresses and software that could recognize many different fonts.
So, back to present day, when you scan a document, how exactly does the software know what it is looking at?
Well, the first step is to cut out artefacts. So, your OCR program can concentrate on the text and nothing else. So, it attempts to remove dust and other various graphics, align the text properly and convert any colours or shades of gray in the image to black and white only, Making the words themselves easy to recognize.
The next step is to figure out which characters are on the page. Simpler forms of OCR compare each scanned letter, pixel by pixel to a known database of fonts and decide on the closest match.
Smarter OCR however takes this step farther by breaking down each character down to constituent elements, like curves and corners and looking for matching physical features and actual letters. You can think of the differences between those two approaches similarly to the difference between raster and vector images.
OCR software can also make use of a dictionary, so it won’t accidentally spit out nonsense words due to inaccurate scanning. Giving OCR software situational information, can further cut down on errors such as telling it to only try to match numbers, if it is reading ZIP codes on an envelope.
even with these tricks however OCR obviously is not perfect.Which you have probably seen for yourself, if you have ever used it.
But with greater processing power and machine learning techniques that allow software to recognize more subtle patterns over time, OCR has become versatile enough to recognize harder to read typefaces, inconsistently printed material and even handwriting.
And free OCR cloud processing services like Google Drive, which has a lot more machine learning capability than your Home PC for which I hope our fairly obvious reasons, have made OCR more accessible than ever. Even Google translate has a feature to translate anything by pointing camera on writing.
You can just point your camera on the writing and Google translate will translate it for you, which is a obvious combination of Google Translate and OCR technology. So, this is it with OCR technology for now. What do you think about this OCR technology, let me know in comments.
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