1 Probabilistic Artificial Neural Network For Probabilistic Artificial Neural Network For Recognizing the Recognizing the Arabic Hand Written Characters Arabic Hand Written Characters Khalaf khatatneh, Ibrahiem El Emary ,and Basem Al- Rifai Journal of Computer Science-2006 Rahma A. Al- Zahrani Presented by King Saud University King Saud University College of Computer and Information Science College of Computer and Information Science Computer Science Department Computer Science Department CSC 563 Neural Network CSC 563 Neural Network Nailah S. Al- Hassoun Prof. Mohamed Batouche Supervised by
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1 Probabilistic Artificial Neural Network For Recognizing the Arabic Hand Written Characters Khalaf khatatneh, Ibrahiem El Emary,and Basem Al- Rifai Journal.
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Probabilistic Artificial Neural Network For Recognizing theProbabilistic Artificial Neural Network For Recognizing theArabic Hand Written CharactersArabic Hand Written Characters
Khalaf khatatneh, Ibrahiem El Emary ,and Basem Al- RifaiJournal of Computer Science-2006
Rahma A. Al-Zahrani
Presented by
King Saud UniversityKing Saud UniversityCollege of Computer and Information ScienceCollege of Computer and Information Science
The task of recognized characters can be separated into two categories:
1. The recognition of machine printed data.
Uniform (size,and position for any given font). Simpler.
2. The recognition of hand written data. Non-uniform( different styles and sizes by different writers and by the
same writers). Difficult.
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There are two kinds of input for Character Recognition : • Off-line character recognition:
1. Takes a raster image from a scanner, digital camera or other digital input source.
2. Off-line processing happens after the writing of characters is complete and the scanned image is preprocessed.
3. Its knowledge is limited to whether a given pixel is on (1) or off (0).
• On-line character recognition:1. Takes (x,y) coordinate pairs from an electronic pen touching a
pressure-sensitive digital tablet. 2. On-line processing happens in real-time while the writing is
taking place. 3. Relationships between pixels and strokes are supplied due to the
implicit sequencing of on-line systems that can assist in the recognition task.
Example: off-line and on-line
handwriting inputs
off-line on-line
Definition contDefinition cont . .
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HistoryHistory
(1990) Hierarchical rule based approach: In this approach the author (Sheik and Al Taweel )assumed :
• A reliable segmentation stage which divide letters into the four groups of position:
1. Initial,2. media,3. final,4. Isolated.
• The recognition system depends on a hierarchical division by the number of strokes. (One stroke letters were classified separately from two stroke letters …etc)
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History ContHistory Cont..
(1990) Segmented structural analysis approach: In this approach the author (Al -Emami and Usher) presented :
• An on-line system to recognize handwritten Arabic words.• Use a structural analysis method for selecting features of Arabic
characters.• The classifications use a decision tree.• Words are segmented into primitives that are usually smaller than
characters. • The system is fed by specifications of the primitives of each character.• The system was trained on 10 writers. They use one tester
who had a recognition rate of 86%.
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History Cont.History Cont.
(1995) Template matching and dynamic programming approach:
In this approach the author ( Alimi and Ghorbel) showed :
• how to minimize error in an off-line recognition system for isolated Arabic characters using template matching and dynamic programming.
• The reference bank of prototypes was prepared.• When new data was presented to the system, the distance
between the prototype and the new data string was minimized using dynamic programming.
• The number of prototypes was varied to see the effect on recognition rates. More prototypes give better accruing.
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History ContHistory Cont..
(1998) Structural and fuzzy approach:In this approach the author (Amin and Bouslama) presented :
• A hybrid system that combine structural and fuzzy techniques.
– Structural analysis : separated between various letter classes to be recognized.
– Fuzzy logic : allowed for variability in people hand writing within the same class.
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History Cont.History Cont.
(2005) Artificial neural networks classifiers: In this approach the author ( Haraty and El-Zabadani) present:
• A system for recognition of handwritten Arabic text using neural networks.
• Their work builds upon previous work that dealt with the vertical segmentation of the written text.
• In fact, They faced some problems like overlapping characters that share the same vertical space.
• They tried to fix that problem by performing horizontal segmented.• The system was tested and the rate of recognition obtained was
90%.
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Optical Character Recognition Optical Character Recognition system (OCR)system (OCR)
• Optical Character Recognition (OCR) :It is machine reads (machine printed /hand written) characters and tries to determine which character from a fixed set of the (machine printed /hand written) characters is intended to represent.
• The goal : is to classify optical patterns (often contained in a digital image) corresponding to alphanumeric or other characters.
• The process of OCR involves several steps including:
• 25 independent writers document• Documents were then processed. The experiments were done on
3 disjoint data sets given by:• 1. Training (37800)= 20 volunteers x 5 iterations x 378
characters• 2. Validation (3780)= 10 volunteers x 378 characters• 3. Test (764) (5 volunteers with different number of characters
in each document).
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• The baseline for recognition accuracy was defined as the average accuracy of the validation and test set of the best PNN architecture
• Probabilistic neural network (PNN) has 7 input nodes in its input layer and 142 nodes in its output layer architecture
ExperimentalExperimental ResultsResults
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Experimental ResultsExperimental Results
• After running our algorithm with a learning rate of η=0.9Train recognition
accuracy rateValidation recognition
accuracy rateTest recognition
accuracy rate Average accuracy rate
99% 98%97%97%
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ConclusionConclusion
•Arabic handwritten recognition is a difficult problem but the AHOCR system will be a step towards a neural network approach to robustly solve it.
• AHOCR is optical handwritten Arabic character recognition (OCR) software capable of producing a fully editable electronic document with current accuracy of 97% for isolated Arabic handwritten character recognition and 96% for Arabic handwritten document recognition.
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ReferencesReferences
• Klassen, T., “Towards Neural Network Recognition Of Handwritten Arabic Letters. ”Dalhousie University (2001). (Link)
• Abuhaiba, I.S. “A Discrete Arabic Script for Better Automatic Document Understanding,” The Arabian J. Science and Eng., vol. 28, pp. 77-94, (2003). (Link)