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Abstract:-
This report explores the application of neural networks to the problem of identifying optically scanned characters in an automated manner.
Input:- scanned images of printed text.
Classification:-Use input values to determine the classification. i.e. is the input the letter A? Data Filtering:-Smooth an input signal.
i.e. noise filtering if any.
Output:-Computer readable version of input contents.
There are several existing solutions to perform this task for English text. The potential benefits of this approach are its flexibility, since it makes no prior assumptions on the language of the text, and it should be possible to extend it to other alphabets.
Introduction:-
We humans have the ability for optical character recognition. In other words, we can differentiate between different characters and recognize them as an A, or B and so on. Can we imbed such ability in software and if we can, how can we?
If we try to understand what exactly happens when we are reading, we will realize that when we see the printed paper an image gets formed on the retina of the eye, some signals are sent to the brain and the brain cells called neurons have something called as intelligence due to which they can recognize the characters.
Now, if we simulate this behavior in software, what we would be actually doing is creating artificial intelligence. This filed of artificial intelligence, which simulates the behavior of a biological neural network in order to perform intelligent tasks, is called artificial neural networks. A typical artificial neural network looks as shown in figure-1.