In this paper we proposed a multiple classifiers system for handwritten Arabic alphabet recognition to investigate if it will really achieve a remarkable increase in the recognition accuracy compared to a single feature-based classifier system result

1. Presentation ContentsPresentation Contents
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS
 The single feature-based classifier system
 Hierarchical Mixture of feature-based classifiers system
Results & Conclusions

2. A Multiple Classifiers System ForA Multiple Classifiers System For
Solving The Character RecognitionSolving The Character Recognition
Problem In Arabic AlphabetProblem In Arabic Alphabet
Authors:Authors:
• Randa I. M. ElanwarRanda I. M. Elanwar
Research assistant , Electronic Research Institute
• Prof. Dr. Mohsen A. A. RashwanProf. Dr. Mohsen A. A. Rashwan
Professor of Digital Signal Processing, Electronic and communication dept, Cairo University
• Prof. Dr. Samia MashaliProf. Dr. Samia Mashali
Head of computers and systems dept, Electronic Research Institute

3. The Optical Character Recognition (OCR) is the task ofThe Optical Character Recognition (OCR) is the task of
transforming language represented in its spatial form oftransforming language represented in its spatial form of
graphical marks (or digitized image of characters) into itsgraphical marks (or digitized image of characters) into its
symbolic representation.symbolic representation.
In case of handwritten characters recognition, modelsIn case of handwritten characters recognition, models
should be used to constrain the character choices toshould be used to constrain the character choices to
overcome the wide variability of hand printing and cursiveovercome the wide variability of hand printing and cursive
script.script.
A pattern recognition algorithm is used to extract shapeA pattern recognition algorithm is used to extract shape
features and assign the observed character into thefeatures and assign the observed character into the
appropriate class.appropriate class.
An expert is focused on those features, which, given aAn expert is focused on those features, which, given a
certain classification technique will produce the mostcertain classification technique will produce the most
certain and efficient classification results.certain and efficient classification results.
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

4. If n classifiers (experts), working on the same problem,
deliver a set of classification responses, then the decision
combination process has to combine the decisions of all
these different classifiers in such a way that the final
decision improves the decisions taken by any of the
individual experts.
It has been found that multiple classifier decision
combination strategies can produce more robust, reliable
and efficient recognition performance than the application
of single expert classifiers.
It has been found that a single classifier with a single
feature set and a single generalized classification strategy
often does not comprehensively capture the large degree
of variability and complexity encountered in many
practical task domains
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

5. In this paper we proposed a multiple
classifiers system for handwritten Arabic
alphabet recognition to investigate if it will
really achieve a remarkable increase in the
recognition accuracy compared to a single
feature-based classifier system result.
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

6. Multiple classifiers systems can be categorized according
to:
1. Architecture
2. Representation level of the output
3. Classifier Ensembles
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

7. 1. Architecture
Parallel
Series
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

8. 1. Architecture (continued)
Conditional Topology: Once a classifier is unable to
classify the output then the following classifier is
deployed
Hierarchal Topology: Classifiers applied in succession
according to their levels of generalization.
Hybrid Topology: The choice of the classifier to use is
based on the input pattern
Multiple (Parallel) Topology
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

9. 2. Representation level of the output
Abstract Output Level: Each of the classifiers identifies
the character in question definitely as belonging to a
particular class.
Ranked Output Level: Each of the classifiers gives a
preference list based on the likelihood of a particular
character belonging to a particular class.
Measurement Output Level: Each of the classifiers
gives a preference list based on the likelihood of a
particular character belonging to a particular class,
together with a set of confidence measurement values
generated in the original decision-making process
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

10. 3. Classifier Ensemble
Ensemble learning refers to a collection of methods that
learn a target function by training a number of individual
learners and combining their outputs. Ensemble methods
combine a set of redundant classifiers
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

11. 1. Combining Strategies
2. Architectures for combining classifiers
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

12. 1. Combining Strategies
Averaging and Weighted Averaging
Non-linear Combining Methods
 Voting Methods
(Majority, Maximum, etc...)
 Rank Based Methods
(Borda Count)
 Probabilistic methods
(Bayesian Methods)
Fuzzy Integral Methods
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

13. 2. Combining Architectures
Boosting
(boosting by filtering, by re-sampling, by re-weighting)
Example:
• Stacked Generalization
Example:
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

14. 2. Combining Architectures
Hierarchical Mixture of Experts
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

15. A database for a single writer consisted of 30
samples (20 for training and 10 for test) of the
Arabic alphabetic characters were used.
In the preprocessing stage, Image binarization
and thresholding were performed.
Recognition results were based upon:
1. A single feature-based classifier system
2.
Hierarchical Mixture of feature-based classifiers system
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

16. The feature used for this single classifier system was
mainly the radial distances
Each character under test is decided to be one of defined
patterns according to the minimum Euclidean distance
between the two feature vectors
The average system accuracy was given by 70.06%
The maximum accuracy was 74.48%
1. The single feature-based classifier system
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

17. 1. The single feature-based classifier system
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

18. Stage 1:
Characters are clustered into groups according to the
number of dots attached to them to work as gating
between redundant classifiers
The same feature is used for recognition in each cluster.
i.e., we now have a
classifier ensemble of individual (Data-varied) classifiers.
Each classifier used different pattern shapes for training
Each character under test is decided to be one of defined
patterns in each cluster according to the minimum
Euclidean distance between the two feature vectors
The average system accuracy has risen to be 78.33%
The maximum accuracy was 82.76%
2. Hierarchical Mixture of feature-based classifiers system
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

19. Stage 1:
2. Hierarchical Mixture of feature-based classifiers system
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

20. Stage 2:
Characters are clustered into groups according to the
number of dots attached to them and the existence of
loops and Hamzas. (8 different classifiers)
The same feature is used for recognition in each cluster
Each character under test is decided to be one of defined
patterns in each cluster according to the minimum
Euclidean distance between the two feature vectors
The average system accuracy has risen to be 80.86%
The maximum accuracy was 85.86%
2. Hierarchical Mixture of feature-based classifiers system
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

21. Stage 2:
2. Hierarchical Mixture of feature-based classifiers system
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

22. PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

23. Stage 2: (continued)
Differing and increasing the number of classifiers enhances the
system accuracy,
New Structural features-based classifiers are added:
 Number and position of the character stroke end points
 Number of vertical and horizontal lines cuts by the
character body
• A fusion technique, weighted average, is used to combine the
decision of more than one classifier during decision making
• Weights are used to weight the output of two or more of
classifiers we have. These weights reflect the degree of
confidence in each classifier, with respect to any input pattern
• The average system accuracy has risen to be 92.25%
• The maximum accuracy was 95.86%
2. Hierarchical Mixture of feature-based classifiers system
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

24. Stage 2: (continued)
2. Hierarchical Mixture of feature-based classifiers system
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

25. PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

26. Stage 3:
Characters are clustered into groups according to the
number of dots attached to them and the existence of
loops and Hamzas. (8 different classifiers)
A New feature-based classifier that uses 45° inclined
lines cuts feature is added
The average system accuracy has risen to be 96%
The maximum accuracy was 98%
2. Hierarchical Mixture of feature-based classifiers system
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

27. Stage 3:
2. Hierarchical Mixture of feature-based classifiers system
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of
Multiple Classifiers
Combining Multiple
Classifiers
 Strategies
 Architectures
Arabic Alphabet
Recognition using MCS
 The single feature-
based classifier system
 Hierarchical Mixture
of feature-based
classifiers system
Results & Conclusions

28. Stage 3:
2. Hierarchical Mixture of feature-based classifiers system
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

29. Stage 4:
Solving the problem of secondaries identification which
causes misclassification at the very beginning of the
system as it leads to wrong gating and using
inappropriate classifier for the input test pattern
The average system accuracy has risen to be 97%
The maximum accuracy was 98.6%
2. Hierarchical Mixture of feature-based classifiers system
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

30. Stage 4:
2. Hierarchical Mixture of feature-based classifiers system
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

31. PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

32. The system stages followed to end up with:
1. Average recognition accuracy of 97%
2. Maximum accuracy of 98.6%
3. The total increase in the recognition accuracy is about
27% from the recognition accuracy achieved by a single
classifier system
4. We were able to achieve high results by proposing the
idea of multiple classifier system (decision fusion) besides
using a classification hierarchy based on the structural
features of Arabic characters.
PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system

33. PresentationPresentation
Contents:Contents:
Authors
Problem Definition
MCS
Paper Objective
Categorization of Multiple Classifiers
Combining Multiple Classifiers
 Strategies
 Architectures
Arabic Alphabet Recognition using MCS

The single feature-based classifier system

Hierarchical Mixture of feature-based classifiers system