A Detailed Guide about Image to Text Technology

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a revolutionary technology that enables computer systems to understand the letters and characters present in an image and extract them.

If you know anything about computers, then you know that they can only process 1s and 0s. There are different standards available that assign unique combinations of binary strings to each character. When we write anything digitally, the computer is simply dealing with the binary strings associated with each character. One of the most popular encoding standards is ASCII. 

Picture to text technology allows computers to ascertain that a certain configuration of pixels in an image are actually characters, and convert them into their binary string. This allows the computer to manipulate that string which results in editable text.

How Does This Technology work?

Picture to text technology basically works due to this thing called OCR or Optical Character Recognition. OCR is an important component of machine learning, and it enables a system to recognize data patterns in an otherwise unordered data set. 

In this case, the unordered data set is a graph of pixels plotted on a two-dimensional plane. With OCR, computers can recognize the shape of characters and then assign them their respective ASCII value. This converts the given text from the image into a digitally editable format.

There are more steps involved in this, but you will have trouble understanding them without an example. So, we are going to go forward while referring to an online tool that can convert image to text. The tool we have chosen is called Imagetotext.info—a top-ranking tool that is simple enough to use—so, you won’t have trouble following the explanation.

There are three major steps involved in converting images to text

  • Preprocessing
  • Character Recognition
  • Post-processing

We will discuss them in more detail now. These steps occur after you input your image into the tool and start the process of extracting the image.

  • Preprocessing

Preprocessing is the stage that comes after the image has been provided to the tool. It has some sub-steps of its own such as binarization, cleaning, and De-skewing. 

In binarization, the contrast of the image is adjusted so that the background becomes either black or white, and the perceived text becomes the opposite color. This allows the tool to easily see the characters when it is time to recognize them.

In cleaning and noise removal, the photo to text converter removes any extra features in the image that may interfere with text recognition. This involves removing distortions from the image and removing stray pixels that may have formed due to dust. It also smooths the edges of the characters to prevent them from getting mixed into each other.

In De-skewing, the image is aligned such that most of the text in it is aligned with the X-axis. This makes the text easier to perceive and thus recognize. 

  • Text Recognition

When the preprocessing is done, the actual text recognition occurs. The characters are recognized based on their features and shapes. Some tools like Imagetotext.info use feature extraction as it is capable of recognizing handwriting and even cursive.

Feature extraction involves recognizing characters based on their characteristics, for example, a capital “A” has the features of a line joining two other lines that are joined at the hip.

Once this feature extraction is done, the recognized text is broken down into tokens i.e., smaller meaningful parts. And this is where we move into post-processing.

  • Post-processing

In this step, the tokens are organized and the tool tries to make sense of them. This requires the aid of NLP (Natural Language Processing). With NLP the tool sees if the extracted text has proper semantics and that it is not nonsense. 

If nonsense is detected, then it means that some mistake has occurred in text recognition. The mistake is usually related to typos and spelling errors which are easily fixed by the tool. Then it shows the user the final form of the extracted text as output.

The user can copy or download this text and save it on their device. In this way, the text that was in the image and could not be edited by word processors is now editable.

What are some Real-Life Use cases of this Technology

Picture to text conversion has a lot of real-world use cases. Its ability to convert text inside an image into an editable format is very much in demand. Given below are some use cases of this technology.

  • Digitizing documents

As advanced as our world may be, we still haven’t moved on from paper media completely. Many companies that have existed for a long time, have huge stores of old records in print form. 

Converting such documents into a digital format used to be a terrible task that required a lot of time and effort. But that is true no longer. Now, with image to text technology, all such documents can be easily converted into digital versions by taking their pictures and running them through a photo to text converter.

Digitizing documents is something that happens almost daily as important documents such as contracts and reports that need to be signed off later need to be converted into digital forms.

  • Data entry/accounting

Data entry tasks and accounting are two places that benefit a lot from jpg to text converters. It is very common for all kinds of businesses and shops to deal with invoices and receipts. 

These are always physical documents and they need to have digital copies as well. They can easily be converted into digital forms with the help of OCR tools.

People who have to do data entry tasks can optimize their own work by reducing the need for manual transcribing. They can simply run the picture of the data they need to enter through an image to text converter and simply copy and paste the digital data into the Excel sheet.

What are its Benefits?

What are the benefits of using this technology? Well, we have already gone through some of them, but if you really want to know explicitly then they are given below. (squibler.io)

  • Saves time

They save a ton of time. Data entry and accounting jobs require the employees to have good typing skills and fast hands. They have to do a lot of manual work that also takes a lot of time. They can ease their tasks and save time by using image to text converters that do a hefty portion of their work for them.

  • Saves money

Where time is saved, money is automatically saved as it allows people to do more work in the same amount of time. This saves people the cost of electricity incurred while keeping the computer systems online on which the people do their data entry work. 

Conclusion

Image to text technology is very useful. We saw how it works, and how accessible it is in the form of online tools. We also saw that it is very easy to use. It has real-life applications and benefits as well. If you want to learn more about similar technologies, you may want to visit the Tech section of our blog.

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