Optical character recognition (OCR) technology has made it very simple than past to extract text from images. With the help of OCR technology, we can do individual types of time-consuming tasks easily. For instance, converting handwriting to text form, searching for images using text queries, replicating documents without typing texts, etc.
From reading the above information, you must be relating it to some magic because of its ability. But, after reading this article you will completely understand how it works.
In order to understand how it works, First, you must know what is in an image and how is it stored on PCs.
It all works on a combination of pixels. A pixel is a very small dot of a particular colour. Multiple pixels combine to make an image. The quality of the picture enhances by the number of pixels it has. For a computer, an image is just a collection of pixels having different colours. It is our eyes that recognize it as an image. A computer just has the information about which pixel has which colour.
So, for computers, there is no difference between text and non-text files. In this way, it is very difficult to recognize text optically. Having all of this knowledge in mind, here is how it works:
The image needs to be processed through different processes before the text is taken out.
Different types of software use unalike combinations of techniques for pre-processing. This is performed to get the least number of errors in results.
The following are the most used techniques in pre-processing:
In this process, every single pixel of an image is converted to black or white color. It makes it crystal clear which pixel belongs to the text or which belongs to the background. It speeds up the process of OCR.
Characters can appear tilted or even upside-down because papers are not aligned perfectly most of the time whenever they are scanned. The purpose of this exercise (Deskew) is to draw text lines horizontally, and then rotate the image to make those lines truly horizontal. This causes the text to be aligned straight so that it can be recognized easily.
This process makes the image smooth to remove noise in the image. Because there is always some noise present in an image that causes difficulty while recognizing text. Despeckling disposes of noise present in the image.
In this process, all the lines present in an image that does not seem to be a character are removed. Because with the presence of unwanted lines in an image, OCR can get confused.
This process is very helpful while scanning images having tables and boxes.
Zoning differentiates between the individual columns of an image. In this way, the text does not get mixed up.
Related reading: 10 Benefits of Data Digitization Outsourcing
A baseline is established for every line of text present in the image. If some pixels were missed in pre-processing, they get caught in processing. The spaces between characters are identified by the OCR software by comparing vertical lines and non-text pixels. Every block of pixel present in these non-text lines is labeled as a token. This process is called Tokenization.
After the tokenization, 2 different strategies are used for character-type identification by OCR software. These are as follows:
Every token is now compared to a set of characters known by the software. Which include numbers, alphabets, symbols, punctuations, etc. The most precise match in pickles up by the OCR software.
In this process, the glyphs and tokens need to be of the same size so that they can be compared easily. Another important thing to note here is that the tokens must be in identical font as glyphs, for transforming handwriting. Matrix matching becomes very fast if the token’s font is known.
Each token is compared according to certain rules that specify the possible character types. For instance, a capital H would likely seem like two vertical lines of the same height joined by a single horizontal line in the center.
The fact that it has command of multiple fonts or sizes makes it beneficial. Moreover, it can be more delicate in differentiating the little variations between a capital I, lowercase L, and the number 1. The drawback? Comparing the pixels in a token to the pixels in a glyph is significantly simpler than programming the rules, which is a much more involved process. If you want to see the conversion result then you can use any image to text converter online.
Once all the tokens are matched, the OCR can show you the results. But it makes some more enhancements before showing results.
All the extracted words are compared to a limited collection of words. The words that dost not match any word are replaced by the closest matching. A lexicon includes, for instance, a dictionary. This can be used to fix words that contain incorrect characters, such as “thorn” instead of “th0rn.”
When OCR is used under a specific niche settings, such as for a legal or medical document, a special kind of OCR may be used that is specially designed for that kind of setting. Under these circumstances, the OCR software may look for specific terms such as maths equation etc.
This process arranges the words in a sentence if there is any type of mistake according to the language’s nature. It is almost the same as the technology which suggests the next word while typing on a keyboard. This solves grammar and punctuation mistakes.
After all these processes, the results are very precise and accurate. There are very less chances of errors.
Related reading: 3 Best Ways to Convert JPG to PDF
After reading all the above information about how OCR works, you can easily note that the results of every OCR software vary from one to other. It depends upon the techniques these tools use. However, I will recommend you JPG to Text Converter tool. This tool requires no installation. You can easily use this tool online as It needs no signup, and its User Interface is very easy to use. The results are very accurate and you can also drag and drop up to 5 images.
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