Keywords : Pattern recognitions


Scansion Text Written in English language and Recognized Printed English Character using Bidirectional Associative Memory Network

Aseel W. Ali

AL-Rafidain Journal of Computer Sciences and Mathematics, 2013, Volume 10, Issue 2, Pages 81-94
DOI: 10.33899/csmj.2013.163487

The fact that English language is a universal language, so it is necessary to propose  a computerized  ways to recognize the texts written in English language, which will simplifies the reading  of any text, treat it, and deal with it in a least possible time.
            The BAM (Bidirectional Associative Memory) network was used to recognize the printed English letters, because it process the small size images of letters in an easy way, also BAM is working in two ways (forward and backward) and store the weights without any amendment, therefore BAM is considered as one of the networks of education controller (Supervised learning).
The recognition of the printed English text was done using the network BAM, while the printed English text was entered to the computer using the scanner, also BAM network used to recognize the letters that have some noise and after training; it gives successful results of recognition about 84.6%.
  The aim of this research is to segment and recognize the printed English text, wither it is clear or it have some noise, Matlab R2008a language is used to accomplish this work.
 

Latin Character Recognition using Neural Networks

Jamal S. Majeed; Aseel Ali; Amar S. Majeed

AL-Rafidain Journal of Computer Sciences and Mathematics, 2007, Volume 4, Issue 2, Pages 143-155
DOI: 10.33899/csmj.2007.164022

The aim of this work is to recognize the printed Latin's characters. In this work two methods for constructing the feature space are used. These methods are Variance and Fractal dimension methods, as a result they have real values for every character in the Latin's language, and from these values they constructed the feature space extractions for every character in the Latin's language. After that, these features are given to the Back Propagation network for recognizing the characters.
The result is a highest recognition for the characters is obtained, it is about 82.75% characters while the unrecognized characters are 17.25.