OCR stands for optical character recognition. It is the driving force behind tools that can extract text from images. In the modern day, OCR is an application of machine learning.
In the simplest terms, it can recognize letters in an image and then extract them. The extraction involves recognizing that a particular formation of pixels is a specific letter. To make it editable, the letter is just written in its ASCII version, which is what computers use instead of text.
Once something is in ASCII, you can edit it with word processing software such as Microsoft Word, and Google Docs. That was a simple explanation, but it didn’t answer any questions. So, let’s take a look in detail at how OCR online works to extract editable text from an image.
How Does OCR Extract Text from an Image?
If we were to divide everything into steps, then there will be three major steps. However, each step has a few sub-steps as well. In this article, we will look at the major steps. The sub-steps will be discussed but not individually.
The first step in text extraction from images is pre-processing. To extract text from an image, the OCR tool has to prepare the latter first.
This preparation does the following things:
- Binarization: the image is converted into two colors only, one dark and one light. The text is made dark so that it shows up clearly against the light background
- Image cleaning: any noise from the image is removed. Noise refers to things like creases and scratches on the image that can reduce the effectiveness of extracting text from an image.
- Image sharpening: all the edges and contours are sharpened so that they do not blend into other parts of the image. This makes the text easier to recognize.
- Segmenting: this partitions the image into sections that have and don’t have any text. The extraction only occurs on the parts that have text.
And with that, the preparations to extract text from image are done. After doing these preparations, the online OCR tool moves on to the next step.
The next step to extract text from image is to recognize characters from the processed picture. This is done with the help of machine learning.
- Character Recognition and Extraction
The OCR online can recognize text from processed images quite easily if they are in a digital font. By digital fonts, we mean fonts that are very clear and shapely. In contrast, we have handwriting and graffiti fonts that can wildly change any letter’s appearance.
Most tools that have OCR online capabilities utilize one of two techniques for character recognition. The first technique is called “Pattern recognition”. This is the technique in which a trained model is used to compare the letters in the image.
This means that the OCR online tool has a database in which it has stored all the characters that it can recognize. It tries to match the new letters with the ones in the database.
The letters are “recognized” as the closest match in that database. This method works well when the fonts are easy to recognize. But, it fails with handwriting and other kinds of unconventional fonts.
The second technique is “Feature recognition”. This is a comparatively power-heavy method as it requires a lot of processing power. Instead of recognizing characters by matching them from a database, it uses rules to figure out what the character is.
These rules have to be created individually for each character and that is why it is a processing-heavy task. An example of a rule would be; two parallel lines bisected with a normal is an “H”. This is slower, but it can recognize characters even from handwriting.
Because of the diversity of these techniques, you can sometimes find tools that have the ability to extract handwritten text from image whereas other tools are not able to do this.
After the characters are recognized and converted to ASCII (which is just jargon for code that computers use to recognize characters). The tools go through the entire text and check if they have made any mistakes. Mistakes such as spelling errors are easy to find and rectify.
However, sometimes they may have recognized an entire word wrongly. In that case, the tool checks for context, and the closest contextually correct word replaces the wrong word. After altering the entire text like this, the extracted text is finally released to the user.
This text is editable, as the user can make changes to it. They can also save or copy it to their word processing software.
And there you have it: a high-level breakdown of how OCR online tools work and extract text from images.
Image-to-text extractors are useful for a variety of purposes such as converting physical documents into digital documents. Recognizing and storing vehicle number plate information and automated systems that scan cards are also real-world applications of OCR.