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Optical Character Recognition Using Neural Networks


Posted by: smart paper boy
Created at: Tuesday 16th of August 2011 04:51:44 AM
Last Edited Or Replied at :Thursday 19th of January 2012 10:33:12 PM
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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 w..................[:=> Show Contents <=:]



Face Recognition Using Artificial Neural Networks


Posted by: nit_cal
Created at: Saturday 31st of October 2009 04:58:30 AM
Last Edited Or Replied at :Saturday 20th of April 2013 12:55:41 AM
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yze complex patterns consisting of a number of elements, those individually reveal little of the total pattern, yet collectively represent easily recognizable objects. The concepts of Neural Networks have been motivated right from its inception, by the recognition that the human brain computes in an entirely different way from the conventional digital computers. The brain modeling techniques opens a new era of Computer System that learns, from experience and uses its experiential knowledge next time. This biologically inspired method is being touted as the wave of the future in computing, reli..................[:=> Show Contents <=:]



Image Processing - Optical Character Recognition


Posted by: nit_cal
Created at: Friday 30th of October 2009 05:13:15 AM
Last Edited Or Replied at :Wednesday 09th of March 2011 02:48:36 AM
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rary as the final result. For humans character recognition seems to be simple task but to make a computer analyze and finally correctly recognize a character is a difficult task. Here we are basically dealing with the â„¢Upper Case Characterâ„¢ recognition.
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Neural Networks for Unicode Optical Character Recognition


Posted by: computer science crazy
Created at: Thursday 17th of September 2009 11:57:50 AM
Last Edited Or Replied at :Thursday 20th of October 2011 11:29:03 PM
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ations with networks employing specific guides (learning rules) to update the links (weights) between their nodes. Such networks can be fed the data from the graphic analysis of the input picture and trained to output characters in one or another form. Specifically some network models use a set of desired outputs to compare with the output and compute an error to make use of in adjusting their weights. Such learning rules are termed as Supervised Learning.
One such network with supervised learning rule is the Multi-Layer Perceptron (MLP) model. It uses the Generalized Delta Learning Rule for ..................[:=> Show Contents <=:]



Emerging Trends In Robotics Downlaod Seminar Report


Posted by: Computer Science Clay
Created at: Thursday 30th of July 2009 06:46:58 AM
Last Edited Or Replied at :Thursday 30th of July 2009 06:46:58 AM
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t.

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Cellular Neural Network


Posted by: Computer Science Clay
Created at: Tuesday 24th of February 2009 12:56:26 PM
Last Edited Or Replied at :Sunday 21st of November 2010 10:46:10 PM
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Boolean functions.

ARCHITECTURE OF CNN

A standard CNN architecture consists of an m*n rectangular array of cells c(i,j) with Cartesian coordinates (i,j) i=1,2¦..M, j=12¦...N.

A cla..................[:=> Show Contents <=:]



Industrial Applications using Neural Networks


Posted by: computer science crazy
Created at: Monday 22nd of September 2008 12:08:04 AM
Last Edited Or Replied at :Thursday 05th of August 2010 12:43:13 PM
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ovide a description of the relationship between cause and effect variables. The benefit ANNs offer over linear models is that they are capable of modelling nonlinear relationships. In fact studies have shown them to be capable of modelling any non-linear function to arbitrary accuracy. Although there exist many different ANN structures, they do possess some common features. They are generally composed of numerous processing elements, termed nodes, which are arranged together to form a network. The most commonly used processing element is one, which weights the input signals and then sums them ..................[:=> Show Contents <=:]



Transient Stability Assessment using Neural Networks


Posted by: computer science crazy
Created at: Monday 22nd of September 2008 12:07:56 AM
Last Edited Or Replied at :Tuesday 19th of October 2010 05:02:49 AM
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for transient stability assessment.
Several approaches for transient stability assessment of power systems such as numerical integration second method of Lyapunov, pattern recognition.

Numerical integration :
In this approach transient stability analysis if performed by simulation. For given operating condition and special and specified large disturbance a time solution obtained for the generator rotor angles, speeds, terminal voltages etc., by examining the swing curves, separation of one or more generators from the rest of the system indicating loss of synchronism is detected. Even fo..................[:=> Show Contents <=:]



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