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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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signals are sent to the brain and the brain cells called neurons have something called as int..................[:=> 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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om the conventional digital computers. The brain modeling ..................[:=> 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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s major applications is Optical Character Recognition (OCR). When the object to be matched is presented then our brains or in general recognition system starts extracting the important features of the object that includes color, depth, shape & size. These features are stored in the part of the memory. Now the brain starts finding the closest match for these extracted features in the whole collection of objects, which is already stored in it. This we can refer as standard library. When it finds the match then it gives the matched object or signal from the standard library as the final result. F..................[:=> Show Contents <=:]



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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gnize even typed optical language symbols is the complex irregularity among pictorial representations of the same character due to variations in fonts, styles and size. This irregularity undoubtedly widens when one deals with handwritten characters.
Hence the conventional programming methods of mapping symbol images into matrices, analyzing pixel and/or vector data and trying to decide which symbol corresponds to which character would yield little or no realistic results. Clearly the needed methodology will be one that can detect Ëœproximityâ„¢ of graphic representations to known symbol..................[:=> 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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m i..................[:=> Show Contents <=:]



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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= -Xij + A(I,j,k,l) Ykl + B(i,j,k,l) + Zij..................[:=> 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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models. Once determined, the dynamic process model of the system can be integrated within a variety of process monitoring and control algorithms. In process control, for example, the model can be incorporated within a model based predictive control (MBPC) algorithm, such as Generalised Predictive Control.

Alternatively, for process monitoring, the residuals (prediction errors) from such models can be analyzed to detect abnormal operation. Such monitoring and control schemes have found widespread application in industry and have led to significant improvements in process operations. Unfortun..................[:=> 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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tern vector or strongly correlated with one another a process of dimensionality reduction is performing to identified significant and hopefully uncorrelated set of components. This is process is called feature extraction. The final step is to determine a function s (x) such that s(x) = { >=0 for a secure x) {<=0 for an insecure x)
this function is called a classifier, at once the classifier obtained, for sample x one can classify the sample as stable or unstable very rapidly. The most important task in the application of pattern recognition is the selection of primary variables because the lo..................[:=> Show Contents <=:]



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