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What is Optical Character Recognition?

Optical character recognition, or OCR, is a revolutionary technology. In simple terms, OCR enables computer systems to look at images and recognize characters in them. 

Normally, when a computer looks at an image, it only sees an array of pixels. The pixels have different colors, no doubt, but the configuration of pixels is meaningless to computers.

With OCR, though, computer systems are able to understand that certain configurations of pixels are characters. Then, they extract them and convert them into machine-readable formats like ASCII or UNICODE. This is why OCR tools are also called image-to-text converters.

In this article, we will take a deep dive into the workings of OCR and its current as well as possible future applications. 

Optical Character Recognition

How Does OCR Work?

OCR is an application of computer vision. Computer vision is the field of study that deals with making computer systems able to “see” things as a human would. OCR is just a limited version of computer vision that is only capable of “seeing” characters. 

Most OCR tools work on the same principles. The steps involved in extracting text from an image are seldom different. There are three major steps, and we will look at what occurs during each step.

  1. Preprocessing

The first step in OCR is called preprocessing. Preprocessing refers to all the things that need to be done before the image-to-text conversion begins.

This includes:

  • Image cleaning
  • Deskewing
  • Image binarization

Image cleaning refers to removing noise. Noise is any unwanted artifact in the image that detracts from its quality. This can include removing stray pixels (i.e., caused by dust or poor scanning).

Deskewing is the act of making the image aligned with the x and y-axis. Sometimes, when an image of a text is taken, there might be slight or moderate skewing, i.e., the text is at an angle. With deskewing, the image is rotated just enough to make the angle 0.

Binarization is the process of removing color from the image. At the end of binarization, the text and its background only appear in black and white. The advantage of doing this is that it makes the text contrast with the background and become more recognizable.

In a typical image to text converter, this process takes place on the back end on the server side. It takes so little time that the end user does event realize that something this big is going on. 

  1. Text Extraction

Text extraction is the step where the OCR tool recognizes the text from the binarized image. 

There are multiple techniques for text extraction. They include:

  • Feature extraction
  • Pattern matching

Of the two, pattern matching is the more simplistic approach. In pattern matching, the image-to-text converter has a database of characters. It recognizes the characters by matching their shapes to the ones it already has in the database. 

This approach is faster, but it is also limited by the number of patterns that are stored, as only text that is sufficiently similar to them can be recognized. If the text has a different style, then it won’t be recognized correctly.

Feature extraction is slower but has a wider range of recognition. It does not try to match entire characters to a pattern. Instead, it recognizes features of a character and uses rules to understand what the character is. This allows the image-to-text converter to even recognize cursive handwriting. 

Another advantage of feature extraction is that it can be supplemented with AI. 

  1. Post Processing

In post-processing, the recognized characters are checked for spelling errors. The characters are reassembled into sentences and worlds. Then, the sentences are checked for grammar. If everything is fine, the tool will start converting the extracted text into machine-readable form. Then, the output is displayed to the user.

So, that’s how an image-to–text converter functions.

What are some Practical Applications of OCR?

Image-to-text converters have various applications. They are used in various fields and systems. Let’s check out some of them.

  1. Conversion of Physical Documents to Digital

The most common application of image-to-text converters is the conversion of physical documents into digital ones. The process of scanning documents and then using OCR on them is much faster than manually transcribing the text into a digital file.

  1. Real-Time Translation

OCR has become fast enough that you can use it for real-time translation. With apps like Google Lens, you can point your camera at a text in a foreign language, and the text will be recognized with OCR and then translated into your chosen language. 

  1. Data Entry Automation

This is an extension of converting physical documents to digital ones. Basically, people in data entry positions can easily enter data from forms, surveys, receipts, and other types of data documents into a digital database. All they have to do is take a picture, use OCR on it, and then copy-paste the digitized data.

  1. Compound Text to Speech Systems

With OCR, visually impaired people can read signs and other things they may find in their daily lives. The OCR app on their phone can scan the text, recognize it, and then feed it to a text-to-speech system. This way, the visually impaired can interact with written texts. 

5. Banking and Finance:

  • Check Processing: Automating the reading and processing of checks.
  • KYC (Know Your Customer): Extracting information from identity documents for customer verification.

6. Retail and E-commerce:

  • Receipt Scanning: Automating the extraction of information from receipts for expense management.
  • Product Labeling: Reading product labels and barcodes for inventory management.

7. Accessibility:

  • Text-to-Speech: Converting printed text into speech for visually impaired individuals.
  • Language Translation: Translating printed text into different languages.


        Optical character recognition is a powerful technology that helps improve people’s lives. It is one of the best things to come about due to advancements in computer vision and artificial intelligence

        It makes many annoying and repetitive tasks much easier to do. The best thing is that this technology is usually available for free.

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