What is the Process by which OCR extract text from images?

What exactly is OCR?

This online application utilizes sophisticated algorithms to extract text from an images with Optical Character Recognition. It is also referred to as an image-to-text tool.

OCR is also known as Optical Character Recognition is a technology that detects the characters of an alphabet and assists in manipulate them using different tools.

Before you begin to work with it you must be aware of its history.

The evolution of OCR

The image-to-text converter that we use today isn't identical to the one developed by the company for the very first time. Its story is filled with twists and turns, bringing our back in time to beginning of the nineteenth century. Children do not come with the ability to read and comprehend written script , even in their native language. They build up muscles slowly and eventually are able to read handwriting.

However, the current OCR Training Dataset is up to in recognizing any type of handwriting However, it is undergoing slow advancements. The story of OCR is rooted in the early days of scanning. The drum scanner and flat-bed scanner revolutionized scanning full-page text.

On the other hand studies were conducted in the 1800s. One of the most important milestones was the creation of the Optophone created by the Kurzweil company to assist people with impairments. While it was in production there were two new items made, namely the CCD Flatbed scanner and the text-to-speech synthesizer. The product was unveiled at a meeting of the National Federation of the Blind. He began selling Optical Character Recognition technology commercially in the year 1978.

Since then, there's been no turning back, and the technology has evolved to enable Artificial Intelligence.

How does OCR extract text from an Image?

OCR is an OCR employs three primary steps to detect any type of writing in an image.

Preprocessing:

In the preprocessing stage, relevant elements from the document are retrieved to be segmented and recognized. Some elements can be left out in accordance with the recognition process and the type of image.

  • The image rotates at the vertical and horizontal angles.
  • Skewing and Slanting are made with different methods.
  • The Hough transformation can be used to calculate an average of text.
  • Additionally, you can make use of directional histograms to calculate the vertical and horizontal slopes of text.
  • To reduce noise The image is then filtered.
  • Thresholding can be used to alter the color of each one of the pixels to black or white.
  • Thinning is a process that reduces the size of characters' pixels to just 1.
  • When we thin, we obtain our text's skeleton.
  • It is also possible to thicken the layer when needed.

Segmentation:

At the stage of segmentation the entire text that has been processed is broken down into words, sentences and characters.

  • In the beginning, the device cuts every part of a sentence according to the probability of making cuts in succession.
  • The most effective cuts will define the space between words that is adjacent to one another.
  • If the word you have obtained is removed or smeared The lexical analysis will find the most probable word of the word.
  • Word recognizers employ techniques for syntactic analysis.

Recognition:

The most vital component of an OCR Datasets because it is where the text is identified and converted into digital format within the computer.

It employs different methods and techniques to identify the letters of a text.

It could be one of the following methods:

  • Soft computing method.
  • Character recognition using MLP.
  • Genetic algorithm that is fuzzy.
  • Generic neural networks.

What is the best way AI can improve efficiency of OCR?

AI is transforming old technology and is developing new ones, which contributes positively to the overall advancement of technology.

It improves and enhances the effectiveness of a traditional by the following methods:

Its AI algorithms allow for extensive pre-processing. The device is educated with an abundance of data, and is able to learn through deep learning.

This is why it gets more intelligent in the process of decision making and in the formation of logic.

AI makes use of dataset for machine learning, or IDP to retrieve diverse and unstructured data , thus increasing the range of OCR.

This technology is being utilized to comprehend different templates of text. When compared with conventional OCR technology, this one gives excellent results.

GTS And OCR Datasets

Global Technology Solutions (GTS) OCR has got your business covered. With its remarkable accuracy of more than 90% and fast real-time results, GTS helps businesses automate their data extraction processes. In mere seconds, the banking industry, e-commerce, digital payment services, Image Data Collection, AI Training Dataset, Video Dataset and many more can pull out the user information from any type of document by taking advantage of OCR technology. This reduces the overhead of manual data entry and time taking tasks of data collection.

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