Image-to-text conversion is the art of recognizing text inside an image and converting it into a word-processor-friendly format. For a long time, scientists have been trying to bridge the gap between the real and digital worlds.
They have had success in some places and a lot of roadblocks in others. One of the major roadblocks was computer vision. Computers are incapable of understanding anything beyond 1s and 0s. It takes many layers of translation to help them understand letters and characters.
In a similar vein, computers cannot understand what is inside an image. To them, an image is only a graph of pixels. Computer vision aims to make computers see and recognize characters and entities inside images.
Image-to-text conversion is possible today due to advances in computer vision. Most of these advancements are due to breakthroughs in AI technology. So, let’s see what role does AI play in image-to-text conversion.
Artificial Intelligence Technologies in Image To Text Conversion
Three major AI technologies enable image-to-text conversion. They are discussed below.
1. Optical Character Recognition (OCR)
Optical character recognition, or OCR, is the branch of computer vision that enables computers to recognize characters inside an image. Before artificial intelligence was developed enough, OCR techniques were incredibly basic.
The earliest iterations of OCR, like the Statistical Machine, Analog Reading Machine, and GISMO, were only capable of recognizing typed or printed text that had a uniform font. These machines have existed since around the 1920s. AI didn’t come into the picture until the late 1990s. Around this time, people started to focus more on the software side of OCR than the hardware.
This paved the way for using artificial intelligence. One of the earliest instances of AI for OCR was in 2005. HP's open-source Tesseract OCR was made available through Google.
Tesseract made use of computer vision and machine learning to enable computers to recognize characters with great accuracy. After that, the advances in deep learning in the 2010s further fueled the improvements in OCR technology.
Nowadays, OCR generally works in the following way:
Images are cleaned.
Images are preprocessed. Binarization–a process where text and background contrast are raised to the maximum to enable easier recognition.
Artificial intelligence algorithms are used to recognize the characters.
Then there is post-processing.
That’s the gist of the entire process. Now, let’s take a look at the other technologies that help with OCR.
2. Machine Learning
Machine learning is the branch of artificial intelligence that enables computers to “learn” from data in a manner similar to human beings. The most common example of machine learning is in image recognition. Computers are varying images of an object, such as a car, and are trained to recognize them. Once the learning is complete, the computer can recognize cars even in images that it has not seen before or trained on.
So, lessons learned from a known data set can be applied to unknown data sets to draw conclusions. In image-to-text conversion, a computer must be able to recognize character glyphs in any style of font.
The most sophisticated systems that use deep learning can recognize even cursive handwriting. This technology is used to recognize the characters in an image with more than 90% accuracy. Nowadays, everyone can access an image to text converter either online or through an app. These tools use a cloud server that can quickly run the AI algorithm on the given images to recognize the text in a matter of seconds.
Cloud computing is required because machine learning models are extremely resource-intensive. You cannot run them reliably on an ordinary computer. Cloud servers, on the other hand, have a lot of processing power, so they are able to use machine learning models quickly.
3. Natural Language Processing (NLP)
The final AI technology that is used in image-to-text conversion is called natural language processing (NLP). NLP is the application of machine learning that enables computers to understand, interpret, and manipulate human language.
As you know, computers can only understand binary. The binary is quite mathematical. Thankfully, human language can be defined by mathematical rules. This means that with enough effort, it is possible to teach a computer to comprehend and manipulate human language.
With machine learning, it is much easier because computers can learn from known data sets. So, you can do something like teach the computer grammar rules and commonly spoken sentences and words. Then, give it a prompt, and it can use its learned knowledge to give a suitable response.
Advanced forms of machine learning, like neural networks and deep learning, are capable of near human-like responses. Most of the AI chatbots like ChatGPT, Gemini, and Bing AI use these technologies.
Anyway, we digress. The role of NLP in image-to-text extraction is to check the recognized text to see if it makes sense. It checks for incorrect spelling, bad grammar, and even missing characters. The errors are corrected using the most suitable choice, and the final result is shown to the user.
Conclusion
So, those are the AI technologies used in image-to-text conversion. We went over their roles in the image-to-text conversion process. Hopefully, you will have a better understanding of the process now.
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