OCR Technology Meaning
OCR technology stands for Optical Character Recognition or Optical Character Reader is the process that converts the image of text into a machine-readable text format.
For example, when we take a picture of some document, any form or any receipt our computer or mobile saves this information as a scan image file.
We actually cannot use this information to edit, we cannot count the words in it or copy this text.
But you know one thing: you can edit, copy, paste or count the words in image form by using OCR technology.
You can convert pdf, images, scanned documents, or files into text form. The process of ocr is most commonly used to turn legal or historical documents into pdfs.
Once image text is converted into editable text you can use microsoft word and docs for editing purposes.
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Advantages of OCR Technology
OCR technology is one the most used type of data entry methods. It has a lot of benefits and you can take great advantage of it:
- It can increase productivity by increasing the speed of information processing.
- It reduces the chances of errors when entering data into the system. Highly accurate technology.
- It lowers the operational and storage cost. This tool helps in cutting down various other costs, like copying, printing, shipping, etc.
- It enhances data recovery.
- It makes the documents editable.
- It helps in saving time.
- Organisations and enterprises use OCR technology to make customers satisfied. As it converts unstructured data into structured data.
- It has ease of usage.
Disadvantages of OCR Technology
Sometimes OCR software is not perfect, so it causes many problems.
Here are some disadvantages that you should keep in mind while using this app to avoid these problems or have any solution to them:
- It can reduce the quality of the document. The point is that what input we provide to the OCR, this will be scanned. If we provide an image text that is not quality-wise, OCR will have a hard time extracting the text. In this case, quality can be lost.
- It can also be time-consuming, for example OCR takes seconds to convert the image to text. So if you have very large images to convert into text it can be time-consuming.
- If the image is not clear . It can affect accuracy. Like the words that are misprinted or hard to access by OCR , they can be written wrong. So you need a proof reading after the text is converted from image to text form.
- It can cause errors , such as when a line is missed or the character word is not recognized correctly .
- One of the disadvantages is that it does not recognize all the languages. Such as Japanese, Chinese, Korean, Arabic and Hebrew.
How Does OCR Work?
OCR ( optical character recognition ) examines the patterns of light and dark that make the words and characters to turn scanned images into text.
This OCR technology software need to recognize the letters and words correctly, to convert in readable and editable text form.
With the advancement of technology OCR is also modified , firstly it was invented for just a specific font recognition but now modern OCR can even recognize different people’s handwriting.
It is very helpful now for different tasks.
The OCR is made up of both the hardware and software . This service aims to convert the physical document into editable softform text for processing.
There are three techniques that are used in this.
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Image Pre-Processing
It is the first step taken in OCR technology.
In this the physical document is converted into a record picture.
The purpose of this step is to improve the image data that suppresses unwilling distortions and enhances some features that are needed for further processing.
The concept is eventually transformed into the black and white rendition, evaluated for bright vs dark characters.
Intelligent Character Recognition
Al analyses the dark areas of the image for recognition of words and numbers. Basically it recognizes a single letter, word and character at a time by using these features.
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Pattern Recognition:
The Algorithm is trained in a way that it can recognize a variety of text, text formats and hand writings.
So when the document is scanned the algorithm compares the already existing text or characters present inside it to the image taken for perfect identity match.
Feature Extraction:
The algorithm uses rules based on specific character properties to identify new characters.
The amount of angle, lines and curves in a letter is a way to recognize letters. It is an example of feature extraction.
Post – Processing:
The final step is correction based. In this the algorithm corrects the flaws in the whole data entry process.
It Corrects the spelling and grammar errors to make it accurate.
The proposed algorithm is based on google’s online spelling suggestion which controls an internal database containing a huge collection of words and letters from all over the web and provides a good suggestion of words that are mis-spelled or wrong written.
