a multiple classifiers system for solving the character recognition problem in arabic alphabet (1 of...
TRANSCRIPT
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
A Multiple Classifiers System For A Multiple Classifiers System For Solving The Character Recognition Solving The Character Recognition
Problem In Arabic AlphabetProblem In Arabic Alphabet
Authors:Authors:
• Randa I. M. ElanwarRanda I. M. ElanwarResearch assistant , Electronic Research Institute
• Prof. Dr. Mohsen A. A. RashwanProf. Dr. Mohsen A. A. RashwanProfessor of Digital Signal Processing, Electronic and communication dept, Cairo University
• Prof. Dr. Samia MashaliProf. Dr. Samia MashaliHead of computers and systems dept, Electronic Research Institute
The Optical Character Recognition (OCR) is the task The Optical Character Recognition (OCR) is the task of transforming language represented in its spatial of transforming language represented in its spatial form of graphical marks (or digitized image of form of graphical marks (or digitized image of characters) into its symbolic representation. characters) into its symbolic representation.
In case of handwritten characters recognition, In case of handwritten characters recognition, models should be used to constrain the character models should be used to constrain the character choices to overcome the wide variability of hand choices to overcome the wide variability of hand printing and cursive script. printing and cursive script.
A pattern recognition algorithm is used to extract A pattern recognition algorithm is used to extract shape features and assign the observed character shape features and assign the observed character into the appropriate class.into the appropriate class.
An expert is focused on those features, which, given An expert is focused on those features, which, given a certain classification technique will produce the a certain classification technique will produce the most certain and efficient classification results. most certain and efficient classification results.
Presentation Presentation 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
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
Presentation Presentation 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
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.
Presentation Presentation 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
Multiple classifiers systems can be categorized according to:
1. Architecture
2. Representation level of the output
3. Classifier Ensembles
Presentation Presentation 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
1. Architecture
Parallel
Series
Presentation Presentation 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
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
Presentation Presentation 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
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
Presentation Presentation 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
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
Presentation Presentation 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
1. Combining Strategies
2. Architectures for combining classifiers
Presentation Presentation 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
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
Presentation Presentation 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
2. Combining Architectures
Boosting
(boosting by filtering, by re-sampling, by re-weighting)
Example:
• Stacked Generalization
Example:
Presentation Presentation 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
2. Combining Architectures
Hierarchical Mixture of Experts
Presentation Presentation 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
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 system2.
Hierarchical Mixture of feature-based classifiers system
Presentation Presentation 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
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
Presentation Presentation 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
1. The single feature-based classifier system
Presentation Presentation 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
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
Presentation Presentation 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
Stage 1:
2. Hierarchical Mixture of feature-based classifiers system
Presentation Presentation 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
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
Presentation Presentation 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
Stage 2:
2. Hierarchical Mixture of feature-based classifiers system
Presentation Presentation 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
Presentation Presentation 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
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
Presentation Presentation 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
Stage 2: (continued)
2. Hierarchical Mixture of feature-based classifiers system
Presentation Presentation 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
Presentation Presentation 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
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
Presentation Presentation 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
Stage 3:
2. Hierarchical Mixture of feature-based classifiers system
Presentation Presentation 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
Stage 3:
2. Hierarchical Mixture of feature-based classifiers system
Presentation Presentation 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
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
Presentation Presentation 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
Stage 4:
2. Hierarchical Mixture of feature-based classifiers system
Presentation Presentation 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
Presentation Presentation 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
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.
Presentation Presentation 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
Presentation Presentation 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
