How Technology has Evolved to Help in Extracting Text from Images
Have you ever thought of real tools that can convert your text into images?
A few decades before, being humble folks with myopic world views, we believed that technological progress could not materialize as we see now.
We believe that human-machine interaction happens in the movies only and isn’t not going to occur even in the distant future.
However, if we introspect our progress, we will come to this appalling conclusion we have almost reached the pinnacle of technology. This whole journey seems like a blink of an eye.
It seems that whole of humanity is sitting on an aircraft faster than a supersonic and safer than a haven.
Nowadays, we see men like Elon Musk aspiring for Mars, Jeff Bezos investing in anti-ageing. Moreover, you can see the inventions of AI in extracting text from images, converting speech into text, and many others.
A brief history of how technology has evolved?
Countless events in the annals of history can be treated as watermarks of technological evolution. Let’s start with the first incident that put civilization on steroids.
It’s the printing press, invented by Gutenberg, earmarked the start of a new era where ideas and books could be available to the masses.
Initially, in Europe, education was in the hands of the church, which patronized the thinking of individuals.
But printed books circulated across the globe, and people were learning new ideas and skills at an increasing speed.
The second most important event in technological progress was the invention of the steam engine. It catalyzed a new era of modernity, opening new jobs and areas of work.
Most importantly, it kick-started the industrial revolution, setting up mechanical factories that consumed power through steam engines.
The third epoch that turned our perceptions and changed our paradigms about machines is the invention of Artificial Intelligence.
Human-machine interaction is the basis of AI. Now, there are specific tools that can convert your image to text, as machines have become so intelligent to understand our writing.
It is equally warming and a chilling prospect. No one had thought of such advancement in technology.
Warming in this way that we can accrue u precedented benefits from these tools. But, it’s fearful if the machines become conscious like us and manipulate our nature instead of being obedient to us.
A pertinent example is Google. At this stage, Google is intelligent as it knows what we are looking for and what we need.
But at this point, we have feelings; that is, it never gets offended by what we are searching for.
How can a tool convert an image into text?
The answer is a single word, “intelligence”. Machines have become powerful by gaining artificial intelligence.
At present, an image to text converter uses OCR technology that recognizes text characters and copy text from image.
Coupled with AI, the algorithms are first trained with a plethora of data regarding the text. In other words, you have to first show different text types and fonts so that it recognizes your text.
It is how you teach a device through machine learning. It is just like teaching a child about different objects. A child understands an object after seeing an object repeatedly.
Once the machine gets the capability of the massive data fed in it, it passes through preprocessing, processing, and post-processing stages.
Before processing, your need to make appropriate changes in the images so that it becomes optimized for the tool. It is just like changing your attire before going to an interview.
The preprocessing stage contains the following phases:
- Changing the colours into binary:
Putting in simple words, binarization is changing the colours into two basic colours: black and white.
An image appearing on the screen consists of very small particles called pixels. Pixels contain the primary colours in different proportions.
A computer recognizes these colours in an Octal number system between 0 to 255. To maintain a perfect balance, you have to keep a balance between black and white.
- Deskewing and despeckling:
Most often, the scanned content does not seem clear. Therefore, it has an imperfect alignment, unnecessary lines, and smudged content.
To remove all these discrepancies, the tool aligns the content in perfect shape and removes noise from it.
- Segmenting the content:
Segmenting is also cutting text into small chunks and tokens, and the whole process is called tokenization.
These segments of text are defined by the text and non-text pixels. It helps to identify the text more clearly. With each segment, a meaningful text is attached.
- Recognizing the patterns and extracting the text:
This is the most vital stage in an image to text tool. In this stage, the tool can use either recognition or extraction techniques. However, it can also use both of these techniques.
Pattern recognition involves comparing your segments to already fed text data. After comparison, matched chunks are selected and used in the text.
In the extraction technique, the tool recognizes the features of a text. Suppose it has to identify “L”, it will recognize it as two lines connected at right angles.
This technique is analogous to humanized cognition of characters. Moreover, its scope is quite vast as it can recognize clumsy handwriting and produce an exact copy of the text
Post-processing involves fine-tuning your extracted text from images to reduce any possible errors.
This stage converts any wrongly spelt words to their closest right words through lexical dictionaries.
In addition, some image to text tools are field-specific and replace general terminology with specific terms like medical and journalism.
Moreover, this stage may check the natural flow of your text and add specific grammatical connectors to enhance your writing.
Wrapping it up:
Technology has dramatically evolved to unveil hidden horizons of progress and unleash the undiscovered potentials of Humans.
Artificial Intelligence is a case in point, as it helps us to change an image to text and tries to reduce an incomprehensible chasm between humans and machines.