Recognition a robust model for the recognition

Recognition of Odia Vowels using Clonal
Selection Algorithm Based FLANN Model  

 

Pushpalata Pujari1  and Babita Majhi2

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 Department of CSIT

Guru
Ghasidas Vishwavidyalaya

 Bilaspur, India

 e-mail: [email protected][email protected]

 

 

Abstract— Recognition of handwritten character is still a challenging problem
due to a number of variations found in writing style. This
paper aims to develop a robust model for the recognition of handwritten
character using functional link artificial neural network (FLANN) as
classifier. The weights of the FLANN classifier are further optimized by using clonal
selection algorithm (CSA) which is inspired by the clonal selection theory of
acquired immunity. Discrete wavelet transform (DWT) is used to extract features
from the handwritten characters and principal Component Analysis (PCA) is used
to further reduce the number of features. The proposed model is applied on dataset
containing 1200 samples of Odia handwritten vowels. recognition accuracy of
85.75% is achieved with the proposed model on test dataset.  

 

Keywords— Handwritten character recognition;   Discrete wavelet transform (DWT); Functional
link artificial neural network (FLANN); Clonal Selection Algorithm (CSA)

 

I.    
Introduction

Two or more techniques can be
integrated with an objective to overcome the weakness of one technique with the
strength of other to produce optimum solution. In literature several research
works have been reported using integration of two or more techniques. In this
paper two techniques FLANN and CSA are integrated to build a robust model for
the recognition of handwritten Odia vowels. DWT transform is used for the extraction
of features from the images of the vowels.

 

 

 

Most of the reported works have
used multilayer network and SVM as the classifier. But the complexity of the
multilayer neural network is more when the number of input features grows on.
Hence in this paper, functional link artificial neural network (FLANN), having
single layer and single neuron is proposed for recognition of hand written Odia
vowels. FLANN has been successfully applied in many applications like channel
equalization 2526,stock market prediction 2728, detection of impulse
noise in images 29 and classification of micro array data 30. Generally
FLANN is having its own updation rule, known as delta learning. In order to
avoid the chances of falling to local minima problem, the weights of FLANN are
optimized using real coded genetics algorithm (RCGA). Wavelet transform has
been applied in applications like tumour tissue identification 19, character
recognition 20,24 license plate localization21,22 and analysis of
protein sequence23. Being motivated by the results of WT, the features of
Odia vowels are extracted using WT in this paper. 

 

The paper is organized as follows: Section I
presents introduction and related work on hybrid models for character
recognition. Section II discusses the dataset, preprocessing, feature
extraction and feature reduction step. Section III describes the recognition
phase. Section IV shows the simulation study and experimental results.
Conclusion and future scope of the research are discussed in section V and VI.

II.    Dataset,
Preprocessing And Feature Extraction

A.     
Dataset

The dataset used in this paper is taken
from computer vision and pattern recognition centre of NIT Rourkela. The
database contains 1200 samples of Odia handwritten vowels. All the samples of
the database belong to twelve classes (1-12). Each vowel (1-12) appears 120
times in the database. 80% of the dataset are used for training and the rest
are used for testing. Few samples of Odia vowels are shown in Fig.1.

 

Fig.1. Samples
of Odia handwritten vowels

B.     
Preprocessing

Pre-processing is a series of
operations which include background noise reduction,
filtering, original image restoration etc which are performed on the input
image. This step is carried out for improving the quality of the image before
the application of other character recognition steps. In this paper first the
data is normalized to a standard size of 64X64 pixels. Then the gray scale
image of the data is generated by using Mean filtering method.  The normalized images of the vowels are shown
in Fig.2.

 

Fig.2. Image of Odia Vowels after normalization

C.    
Feature extraction by using
discrete wavelet transform

Feature extraction
is carried out to find important features to be used in the recognition phase.
In this paper DWT based approach is used for feature extraction. In DWT, a
time-scale representation of the digital signal is obtained using digital
filtering techniques 313233 and 34. Due to the better energy compaction
property of wavelet transform it provides substantial improvement in picture quality
at high compression ratio. In DWT 1-D wavelet transform is applied along the
rows of the image for obtaining 2-D DWT decomposition. The results obtained are
then decomposed along the columns which splits the given input image into four
decomposed sub band images. The sub band images are represented as LL, LH, HL
and HH frequency bands.  2-D DWT
decomposes the image into two parts: approximation and detailed part.
Approximation part contains a low frequency sub- band LL and detailed part
contains three high frequency sub bands LH, HL and HH.  The process is repeated to obtain multiple
scale wavelet decomposition.

          

Any given signal
can be decomposed by DWT into a set of basic functions called wavelets. It is
realized from a single prototype wavelet ?(t) by mother wavelet using dilations and shifting. DWT requires a two-dimensional scaling function, ?(u, v) and three two-dimensional
wavelet functions, ?H(u,v), ?V(u,v), and ?D(u, v). They are represented as
the products of two one-dimensional functions,

 

                                                        (1)

 

where ?(?) is a one-dimensional
scaling function and ?(?) is a one dimensional
wavelet function. These wavelets measure intensity functional variations along
different directions.  ?H measures variations along
columns, ?V measures variations along
rows and ?D measures variations along
diagonals.

 

The
two-dimensional discrete scale and translated basis functions are defined as

 

                          (2)

   (3)

 

Where j is a scale and m, n are the translation quantities. The
transform of image f(u,v) of size M X N is expressed as

 

      (4)

                                                                      (5)

 

Where  represents
approximation part of image f(u,v) and  represents
horizontal, vertical and diagonal parts.

III.  Classification

 

A)    
Functional Link Artificial
Neural Network

 

Functional link artificial
neural network is a higher order neural network (HON) 25262728 used for
several applications like classification, planning, system identification,
intelligent pressure sensor, electric load forecasting, intelligent sensor,
insecurity estimation etc. In this paper FLANN is used for the classification
task. FLANN has only one neural element and link which makes it simpler than multilayer
artificial neural network (MLANN). FLANN needs less number of iterations and
computation in training phase. It can handle non-linear problems with faster
rate of convergence with less complexity. In FLANN the dimension of input space
is increased by extending the input vectors with a suitable enhanced
representation of input vectors. A single layer model based on trigonometric
expansion is presented below

 

For the input pattern

                                                         
      (6)

 

The enhanced pattern using trigonometric function for   is
represented as

 

      (7)

where

 

Fig. 3 shows the architecture of FLANN classifier. The
extended inputs are multiplied by a set of weights calculated by using eq.  (8). The outputs obtained are summed to
produce the estimated output. The estimated outputs are compared with the
desired class value. The errors generated are minimized by adjusting the
weights of the FLANN by using RCGA.

 

Fig. 3 . Architecture of FLANN model as
a classifier for Odia vowels

 

 

A)     Weight optimization by RCGA_FLANN model

 III. Clonal Selection Algorithm (CSA)

 

Clonal selection
algorithm is an optimization technique which behaves same as an organism
behaves against a pathogen. It is based on natural theory of evolution inspired
by Darwinian principles. It undergoes through the phase of selection, reproduction
and mutation. In
reproduction phase the child is exact copy of the parent. Mutation is carried
out to maintain diversity in population. an 17 Clonal selection algorithm is
a population based  algorithm which comes
under the field of artificial immune systems. The algorithm learns adaptively
in the same way the antibodies learns the features of antigen and behaves
accordingly. The algorithm starts by creating a set
of possible candidate solutions. The affinity is calculated using the antibodies.
The affinity determines the antibodies to be cloned for the next stage.  The algorithm clones most simulated
antibodies. After cloning of anti bodies mutation is carried out on the cloned
values with a mutation ratio. After mutation the affinities of the antibodies are
evaluated again. After certain generation the affinity with the lowest value
provides the desired solution to the problem. The generic steps of CSA are listed as follows:

 

 Step 1:  Initialize a set of antibodies randomly where each
antibody represents a candidate solution for the specified objective function.

 

Step
2: Using each antibody calculate the affinity value of each candidate
solutions.

 

Step 3:
Starting from the lowest affinity to highest affinity sort the antibodies.

 Step 4: Clone the antibody having lowest
affinity with other antibodies with some predefined ratio.

 Step 5: Mutate the cloned antibodies with mutation
ratio. 

 Step 6: Reevaluate the affinity of each
antibody.

 

Step
7: Repeat the steps from 3 to 6 until the desired criteria is reached.

 

  IV.
Weight Optimization
 of FLANN Model using Clonal Selection
algorithm

 

The steps involved in optimization 17 of weights of
FLANN model using CSA are as follows

Step1:
Initialize the weights of the FLANN model from -1 to +1. Each weight of the FLANN
model represents an antibody. Generate m number of weight vectors where each
weight vector represents a candidate solution to the problem.

Step2:
Take ‘x’ number of input samples. Apply the input samples to the FLANN
classifier. Evaluate the output of the FLANN classifier using the weight vector
and   input samples.

Step
3: Generate error terms      using output  obtained from the
model and target output   for   sample and  vector using (2)

Step
4: Calculate mean square error (MSE) for each set of weight vector using (3). Best
fitness is based on the minimum MSE obtained.

The
goal of CSA is to minimize MSE to get a suitable set of weights for FLANN classifier.

Step
5: Select the weight vector having minimum MSE.

Step
6: Clone the selected weight vector with a pre defined probability.

Step
7: Randomly choose the weight vectors and mutate the weight vectors with
mutation probability

Step
8: Repeat the step 2 through 6 until stopping criteria is reached.

 

  
V. Simulation
Study for FLANN- CSA Model

 

The experimental work is
implemented using MATLAB software. 1200 number of handwritten Odia vowels is
used for the experimental work. 90% of the dataset is used for training and rest
is used for testing the proposed model. The vowels are categorized into one of twelve
categories. In the training phase fivefold cross validation is used to build
the model.  In the preprocessing phase
the images of the vowels are normalized into 64 X 64 pixels and median
filtering is applied on the images of the vowels. For generating feature vector
DWT is used in the feature extraction phase. The dimension of the generated feature
vector is further reduced using PCA from 2519 to 33.  In recognition phase FLANN is used as a
classifier. The reduced features are applied as inputs to the FLANN model. Initially
weights are randomly generated within the range from -0.5 to 0.5 and sigmoid
activation function is used for obtaining the outputs. Errors are generated
using actual and predicted outputs. The weights of the FLANN classifier are
adjusted using CSA algorithm.  The error
terms obtained from the model are used for the computation of Mean square error
(MSE). In the proposed model 1000 number of generation taken. The confusion matrix obtained
for FLANN_CSA model with equal number of classes is shown in figure 5. Table 1
shows the recognition accuracy of FLANN_CSA model for each class. From
experimental result the recognition accuracy is found to be 85.25%.  

 

 

 TABLE 1. Confusion Matrix Of FLANN_CSA
Classifier During Testing

 

 

Class1

Class2

Class3

Class4

Class5

Class6

Class7

Class8

Class9

Class10

Class11

Class12

Class1

90

3

1

0

0

0

1

0

0

0

0

1

Class2

2

89

1

0

1

0

1

0

0

1

0

1

Class3

1

2

82

3

2

2

0

0

2

1

1

0

Class4

1

1

8

73

2

3

0

2

0

2

2

2

Class5

0

1

2

2

76

3

2

2

2

2

2

2

Class6

2

2

3

2

3

70

2

1

3

2

2

3

Class7

0

1

0

1

1

2

81

3

1

2

2

2

Class8

2

0

1

1

2

1

2

85

2

0

0

0

Class9

0

0

1

1

0

0

1

1

89

2

1

0

Class10

1

0

2

2

2

1

0

2

3

82

1

0

Class11

1

0

0

1

1

0

1

0

1

0

89

2

Class12

2

1

2

3

2

0

2

0

1

1

2

80

TABLE 2. Classification accuracy Of The FLANN_CSA model

 

Class

No. of Samples

No. of Correct
Prediction

Accuracy %

Training

Testing

Training

Testing

Training

Testing

Class1

96

24

90

22

93.75

91.67

Class2

96

24

89

21

92.71

87.5

Class3

96

24

82

20

85.42

83.33

Class4

96

24

73

17

76.04

70.83

Class5

96

24

76

18

79.17

75

Class6

96

24

70

16

72.92

66.67

Class7

96

24

81

19

84.38

79.17

Class8

96

24

85

21

88.54

87.5

Class9

96

24

89

22

92.71

91.67

Class10

96

24

82

20

85.42

83.33

Class11

96

24

89

22

92.71

91.67

Class12

96

24

80

19

83.33

79.17

Average Accuracy

85.59%

82.29%

 

IV.
Conclusion

 

In
this paper two techniques: FLANN and CSA are combined to produce optimum
solution for the recognition of Odia handwritten vowels. Before recognition
preprocessing, feature extraction and feature reduction steps are carried out.
DWT is used for extraction of features form the vowels and PCA is used to
further reduce the number of features from 2519 to 33. The reduced features are
applied to FLANN classifier in the recognition phase. The weights of FLANN classifier
are adjusted using CSA to obtain a suitable set of weights which provides
optimum solution for recognition of Odia vowels. From the experimental result it
is observed that the proposed system which uses a combination of DWT transform,
FLANN classifier and CSA based optimization technique has achieved 82.29% accuracy
on test dataset. Still the recognition accuracy of the system can be improved
applying more robust techniques in feature extraction and recognition phase.