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Arabic characters recognition neural networks

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Anas alhamdan

on 4 May 2011

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Transcript of Arabic characters recognition neural networks

Arabic Text Recognition Using Neural Networks Difficulties General Difficulties •Presence of lines and other non-character objects.
•Presence of noise or salt-and-pepper in the scanned image.
•Linguistic problems. Handwritten Text Specific Difficulties •Variety in character size.
•Variety in writing instrument
•Different writers or even the same writer under different conditions may slant their letters differently.
•Translation problems.
Arabic Text Specific Difficulties •Arabic characters can have more than one shape according to their position in a word whether at the beginning, middle, final, or stand alone, as shown in Figure 1.
•Different writers or even the same writer under different conditions will write some Arabic characters in completely different ways, as shown in Figure 2. •Other characters have very similar contours and are
difficult to recognize especially when non-character
and external objects are present in the scanned
image. Figure 3 shows a list of such characters.
•Handwritten Arabic characters depend largely on contextual information. There are a lot of handwritten characters that can be classified into two or more different classes depending on whether you look at them separately, in a word, or even in a sentence. For example, (zero) in Arabic looks exactly
the same as a dot ‘.’.
Methods Perception net (natsha)
Single layer
inputs are a Matrix of 50x50 Pixels
Each output neuron has a bias
Bias is the orginal value for neuron The total input to the jth neuron output neuron is calculated by:
Y=(∑WijXi)+ bi (1)


Where, bj is bias weight for the jth neuron. Weights are an given initial zero values

Wjnew= Wjold+ α (tj - yj )Xj (2)
bjnew= bjold+α (tj - yj ) (3)

Where:
α = Learning rate
tj and yj = Target and actual values for the jth output neuron, respectively.

The values for xi, yj and tj are either +1 or -1. Multilayer BP-NN A multilayer neural network using back propagation training policy.
- It consists an input layer, one hidden layer, and output layer.

-the input layer consists of 50x50 pixels
- the hidden layer consists of 20x20 pixels

The output layer consists of as many neurons as the required number of characters to be recognized.

The output layer has a bias weight The sigmoid function evaluated as the net output value
for that neuron, which can be expressed mathematically as:
σk= (tk – yk).f ́ (yk) (4)
Where, tk target output for the kth output neuron. Wik new =Wik old +α.σk .Zj


a is the learning rate, typically in the range of 0 to 1. Tests And Results Training and Testing times for the network Testing rates with no addition of noise Testing rates with addition of 10% noise Testing rates with addition of 20% noise Error Rates Presentation By:
Anas Al Hamdan Omar Al Smeheen Eyad Jbran
References 1- Back Propagation Neural Network Arabic Characters
Classification Module Utilizing Microsoft Word
Hamza, Ali A. Faculty of Computer Science and Information Technology, Al- Isra Private University. 2008
2- Arabic Text Recognition
Ramzi Haraty and Catherine Ghaddar, Lebanese American University, Lebanon 2004.
3- Arabic Character Recognition using Artificial
Neural Networks and Statistical Analysis
Ahmad M. Sarhan, and Omar I. Al Helalat, 2007 Thank You
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