How OCR Technology Is Revolutionizing Image To Text Conversion

Image-to-text conversion has been a manual task for a long time. If people wanted to extract text from an image and put it into a digital file, they had to write the text manually. That was and still is immensely inefficient due to the labor and time involved.

This was mainly because computers just were not capable of looking at pictures and figuring out there was text in them. To them, text is always represented through ASCII or UNICODE representations. Specific arrangements of pixels don’t mean anything to computers.

That is until the advent of OCR. OCR is the by-product of artificial intelligence but more specifically of image processing. Image processing was a major breakthrough in making computers pick out and recognize shapes and figures inside images.

How Did OCR Revolutionize Image-to-Text Conversion

Before we can discuss that, let’s take a moment to understand what OCR is. OCR is an application of image processing through AI. It allows computer systems to understand that certain patterns and arrangements of pixels are characters and letters. This is done through machine learning.

Once the system is capable of recognizing characters, it is further programmed to extract them and convert them into their ASCII or UNICODE representations. Those representations can be understood and manipulated by word processors. In this way, the text is transformed from image format to machine-understandable text format

This opened the way for automated image-to-text conversion. Nowadays, there are plenty of tools you can find online that can convert images to text. They are commonly known as “image to text converter”. They work by taking an image as input and then extracting the text that users can either copy or download.

Now, let’s discuss the implications of this, i.e., how OCR has revolutionized image-to-text conversion.

Highly Accurate

OCR has made text extraction from images a highly accurate task. Manual extraction didn’t have problems with text that was written in clear fonts, but unorthodox fonts and handwriting posed quite a problem.

Not with OCR though. OCR-based text extractors can understand all kinds of fonts including handwriting. That’s because they are trained in a wide variety of texts. They can utilize a technique called feature extraction in which rather than recognize the shape of characters they recognize their features.

For example, the letter “H” has the following features:

● It has two vertical, parallel lines
● They are joined together with a horizontal line, near their midsection

As long as these features are present the OCR tool will always recognize the letter as H. This accuracy is great because it improves the feasibility of automation.

Automated Extraction

Automation is great. Automation makes menial tasks easy to do and it also removes the factor of human error from the equation. With OCR this is possible. You can set up a simple script using an OCR API to automatically extract text from images and save them as a digital file.

You can also do this without APIs too. But in that case, ensure that the tool you select lets you extract text as often as you like. You can extract text from a lot of documents in a short amount of time.

By using OCR for automation, the business and work environment document flow can be sped up. In this way, a huge bottleneck is loosened up and work becomes more productive as well.

Can Work with Different Languages

OCR tools are not bound by language. They can be trained to recognize letters of all languages and extract them. Translating the letters into machine-readable text is not a problem either because Unicode has representations for all kinds of characters.

This opens a lot of avenues because being able to convert images into text with different languages allows for things like real-time translation. This opens a lot of accessibility options that help organizations work across borders.

This is revolutionary in its own right. But here is where it revolutionized OCR. The sheer amount of stuff you can do with it is amazing. Google Lens, for example, lets you translate text inside images in real time using OCR. So, it is impressive.

Has Many Real-World Use cases

Many different use cases of image-to-text conversion have been enabled due to OCR. The biggest one is the conversion of physical documents into digital ones. Just over a decade ago businesses used to have all of their documentation in the form of physical media and handwritten files. In fact, even now, many documents are still used in physical form because of how ingrained they are in people’s lives (e.g., receipts and bills).

With OCR, image-to-text converters can understand these handwritten documents and create digital copies. These copies are easier to find and sort, which helps immensely with record keeping.

OCR has allowed security and verification systems to instantly check whether the presented documents are real or not. This is used in airports, police stations, and other types of government institutes to verify the identity of people by scanning their identification cards.

Conclusion

So, there you have it, how OCR technology has revolutionized the image-to-text conversion industry. It has made them vastly superior when it comes to accuracy and automation. Consequently that has also increased the number of real-world use cases of image-to-text conversion. So, that’s how image-to-text converters have become much better at their jobs due to OCR.