Learning to read is basically a process bin which you train your brain to add meaning to abstract, arbitrary symbols. Fortautnely, the brain is a pattern-seeking organ that takes to this in a duck-to-water kind of way.
Even more fortunately, you probably grew up with a support structure. A structure that helped you determine correct patterns of grammar and corrected you when missing or misidentifying symbols.
That’s the gist of OCR (optical character recognition). The big difference is that a computer program doesn’t have a natural instinct for language patterns and needs to be reinforced a lot harder.
Modern optical character recognition software uses libraries of previous attempts to provide that reinforcement.
OCR Optical Character Recognition
The most common usage for OCR is in transferring images and pdfs into more usable text. Like text editors, OCR relies on a series of guesses to make sense of what is essentially spaces and blotches.
To a computer, letter characters are a product of code, each ascribed to a different numerical integer. When you work with a text file, you move the letters around according to the language patterns in your head. The computer moves bits of data around remembering what goes where but with no syntax.
Teaching the computer how to use language is a farce. Check out a text bot to see how well that goes. Not, it’s better to have the computer scan the image and then compare the images to other images that already have code underpinning them.
This enables the computer to make educated guesses about the spacing of characters, the arrangement of letters, and ultimately the content of words and sentences.
Most fonts (basically, everything but Courier) have irregular spacing for each letter. Even the spaces between those letters varies. This makes translating any given shape into a ‘letter’ more challenging than you might think.
It’s like if you saw a message in Cyrillic and were asked, not for the meaning of the word, but just to repeat the characters. Without knowing the names of the characters and being versed in them, you would end up describing them with touchstones in a language you do know.
This means that software needs to be able to identify characters in various languages. To this end, it also needs access to libraries that provide context fitting the subject matter. You’ll find OCR libraries for legal applications, academic studies, and math-heavy disciplines filled with special characters.
This is when you want to turn to specialized libraries like those used by C# Tesseract OCR. Additional libraries can be downloaded into an existing program. With each addition, the software gains a new series of documents with which to compare data.
In the same way that a student is only ever as good as their teacher, an OCR program is only as good as the data it draws from. Higher quality scans and more comprehensive libraries are necessary to empower and grow OCR optical character recognition.
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