International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. 80-88 © IAEME
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ODIA HANDWRITTEN DIGIT RECOGNITION USING SINGLE LAYER
PERCEPTRON
Jagyanseni Panda1
, Manaswinee M. Panda1
, Aryapriyanka Samal1
, Niva Das1
1
Department of Electronics and Communication Engineering
ITER, Sikha ‘O‘ Anusandhan University, Odisha, India.
ABSTRACT
The goal of handwriting recognition is to interpret the contents of the data and to generate a
description of that interpretation in the desired format. The paper develops an efficient way for
recognition of odia numerals by using a non-linear classifier. Here the gradient and curvature feature
of odia numerals calculated. Then this processed through a non linear classifier for classification.
The classification results using gradient and curvature are better than other method. This paper
presents recognition of odia numeral using single layer perceptron where complexity is reduced and
by using gradient method accuracy is more. Experimental results demonstrate that on a database of
100 digit patterns written by 100 different people perceptron based classifier which exhibits 85%
accuracy.
Keywords: Curvature Feature, Gradient Feature, Numeral Recognition, Neural Network, Principal
Component Analysis.
I. INTRODUCTION
Over the years computerization has taken over large number of numeral operations, one such
example is offline handwritten numeral recognition. Automatic handwritten odia digit recognition
still remains a challenging task for a computer although a lot of research has been done on this topic.
Recognition of hand written numeral or character is difficult because different people have different
writing style and some characters closely resemble each other. Electronics media has recently
gaining popularity because it replaces the paper and is fast to access [1] .So offline character
recognition is an active research area. Automatic digit recognition system usually follows two steps:
feature analysis and pattern classification.
Feature extraction is that of extracting from the raw data the information which is most
relevant for classification purposes, in the sense of minimizing the within-class pattern variability
while enhancing the between class pattern variability. Considerable research work has been carried
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International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
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out in this area and various methods have been proposed for the classification of handwritten digits.
The methods include principal component analysis (PCA) for feature reduction. India is a multi-
lingual multi-script country and there are twenty two languages. Among Indian scripts, Devnagri,
Tamil, Odia and Bangla have started to receive attention for OCR related research in the recent years
[2, 3].
The basic features of offline character recognition are:
• Flexibility: it should recognize a large number of character patterns.
• Efficiency: it should be efficient.
• Automated Learning: it should have automatic learning capability.
Online Adaptability: it should have the capability to gather new knowledge of different
writer-specific handwritten patterns as it operates.
In 2009 Subhadip Basu et al. proposed a hierarchical approach to recognition of handwritten
Bangla character. Though the bangla characters contain the matras. The matra hierarchy has three
zones; i. e. the upper zone, middle zone and lower zone. The character segments obtained from the
said three portions are recognized separately through appropriate pattern classifiers. Literature
reports some work related to recognition of handwritten digits of Indian scripts [4],[5],[6],[7].
Technique for recognition of handwritten Hindi numerals based on modified exponential
membership function fitted to fuzzy sets is presented in [8]. In this paper we have proposed single
layer perceptron for classification. In the first stage of recognition the feature selection is done.
Gradient features and curvature feature have been considered for this purpose. Then these feature are
used to train the single layer perceptron network.
The paper is organised in 5 Sections, Section 2 presents a brief overview of data collection
and feature extraction method. Section 3 deals with the classification task using two single layer
ANN structures. The simulation results are given in section 4. Section 5 presents the conclusion.
II. DATA SET AND FEATURE EXTRACTION
Data set of odia handwritten numerals 0 and 2 is created by collecting the handwritten
documents from different people. Data collection is done on a sheet specially designed for data
collection.
Fig.1. General Architecture of handwritten character recognition system

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. 80-88 © IAEME
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The feature extraction process consists of procedures for gradient calculation, curvature
feature calculation, feature vector generation and dimension reduction of the feature vector. Each
procedure is described in succeeding subsections.
2.1 Calculation of Gradient
A gray scale image is generated from an input binary image, and the gradient is calculated as
described below.
(1) Size normalization is applied to a binary character image so that the image has standard width
and height.
(2) Mean filter of size 3×3 is repeatedly applied r times to obtain a gray scale image.
(3) The gray scale image is normalized so that the mean and the maximum of the gray scale are 0
and 1, respectively.
(4) Roberts filter [9], [10] given by Eq. (1) and Eq.(2) is applied to each pixel g(i; j) of the
normalized image to calculate the gradient.
∆‫ݑ‬ ൌ ݃ሺ݅ ൅ 1, ݆ ൅ 1ሻ െ ݃ሺ݅, ݆ሻ
∆‫ݒ‬ ൌ ݃ሺ݅ ൅ 1, ݆ሻ െ ݃ሺ݅, ݆ ൅ 1ሻ
Direction:ߠሺ݅, ݆ሻ ൌ ‫݊ܽݐ‬ିଵ
ሺ
∆௩
∆௨
) (1)
Strength: ݂ሺ݅, ݆ሻ ൌ √∆uଶ ൅ ∆vଶ (2)
2.2 Calculation of Curvature
The procedure for curvature calculation, based on bi-quadratic interpolation is described below.
Fig. 2 neighbourhood of a pixel x0.
The curvature c at x0 in a gray scale image is defined by;
( )32
'1
''
y
y
c
+
=
(3)
Where y=g(x) is equi-gray scale curve passing through x0, (x, y) is the spatial coordinates of
x0, y’ and y’’ are the first and the second order derivatives of y, respectively. The derivatives y’ and
y’’ are derived from bi-quadratic interpolating surface for the gray scale values in the 8-
neighborhood of x0. The bi-quadratic surface is given by;
x4 x3 x2
x5 x0 x1
x6 x7 x8
j
i

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 4, April (2014), pp. 80-88 © IAEME
83
[ ]




















=
2
222120
121110
020100
2
1
1
y
y
aaa
aaa
aaa
xxz
Then the equi-gray scale curve passing through x0 is given by
( ) ( )
000010
2
20
0111
2
21
2
02
2
22
=−++
+++++
faxaxa
yaxaxayaxa
(4)
Differentiation of both sides of Eq. (4) by x leads to
( ) ( ){ }
( ) 0111
2
210212
2
22
10201120
2
22
2
222
'
axaxaaxaxay
axayaxayxa
y
+++++
+++++−
=
(5)
Substituting the coordinates (0, 0) of x0 to (5), the value of y’ at x0 is given by
0110 /' aay −=
(6)
Similarly, the value of y’’ at x0 is given by
01
3
2001
2
1110010210
2
/)(2'' aaaaaaaay +−−= (7)
Solving the simultaneous linear equations (3) holding for 8-neighbor of x0, the coefficients of the bi-
quadratic surface are given by
ܽଵ଴ ൌ ሺ݂ଵ െ ݂ହሻ/2
ܽଶ଴ ൌ ሺ݂ଵ ൅ ݂ହ െ 2݂଴ሻ/2
ܽ଴ଶ ൌ ሺ݂ଷ ൅ ݂଻ െ 2݂଴ሻ/2
ܽ଴ଵ ൌ ሺ݂ଷ െ ݂଻ሻ/2
ܽଵଵ ൌ ሺ݂ଶ െ ଼݂ሻ െ ሺ݂ସ െ ݂଺ሻ/4 (8)
The coefficients a10 and a20 are, respectively, the first and the second order partial derivatives
of f(x, y) regarding x; a01 and a02 are similar partial derivatives regarding y, and a11 is the one
regarding to x and y. Substituting Eqs. (6) And (7) to Eq. (2), the curvature is given by
‫ܥ‬ ൌ െ2ሺܽଶ
଴ଵ
ܽ଴ଶ െ ܽ଴ଵܽଵ଴ܽଵଵ ൅ ܽଶ
଴ଵܽ଴ଶሻ/ሺܽଶ
ଵ଴ ൅ ܽଶ
଴ଵሻଷ/ଶ
(9)
2.3 Generation of Feature Vector
A feature vector is composed of the strength of gradient accumulated separately in different
directions as described below;
(1) The direction of gradient detected is quantized to 32 levels with
గ
ଵ଺
interval.
(2) The normalized character image is divided into 81 (9 horizontal × 9 vertical) blocks.
(3) The strength of the gradient is accumulated separately in each of 32 directions, in each block,
to produce 81 local spectra of direction.

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
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(4)The variable transformation (y = x^0.4) is applied to make the distribution of the features
Gaussian-like [11], [12].
2.4 Dimension Reduction
The required processing time and storage can be reduced by the dimension reduction
employing the principal component analysis (KL transform). The principal component analysis is a
typical dimension reduction procedure based on the orthonormal transformation which maximizes
the total variances, and minimizes the mean square error due to the dimension reduction. It is shown
that the dimensionality can be reduced to without sacrificing the recognition accuracy in
handwritten numeral recognition employing the feature vector of size 400 detected from the gradient
of the gray scale[13],[14].
III. CLASSIFICATION
ANN has been considered as one of the nonlinear classifiers for digit recognition. The most
common architecture of ANN is the multi layer feed forward network. But due to its multi layered
structure, the complexity is more as compared to other single layer feed forward networks. Hence in
this paper we have proposed a single layer perceptron based scheme for the recognition task.
3.1 Single Layer Perceptron Structure
The basic structure of a single layer perceptron is given in Fig.3. It can be viewed as a single
"neuron" with multiple inputs that generates an output signal.
Fig.3: Single layer perceptron network
(10)
The value of this output depends on the relative strengths of weighted input signals. The perceptron
output can be expressed as:
(11)
where, is the adaptive weight vector is
the input signal vector, and b is the bias term. The most commonly used activation functions are
sigmoid & hard limiter. The perceptron weights are updated according to:

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
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(12)
Where, η is the learning rate parameter less than 1 d(n) is the desired output or target.
IV. SIMULATION RESULT
To test the performance of the proposed single layer perceptron based scheme simulation was
carried out on MATLAB 2007b platform. 100 isolated handwritten odia digits were collected for the
purpose of training and testing. First, the gradient feature was calculated then the curvature feature
was calculated. Using the procedure in Section 2. For each data set a feature vector consisting of
2592 features was generated. Then using PCA the gradient and cuvature features were reduced to 95,
97 numbers. The feature vector comprising of 95, 97 features was used to train both the ANN based
structures.
Out of 100 isholated hand written odia digits 50 numbers of 0 and 50 numbers of 2 were
collected. For the single layer perceptron, each training set comprises of 95 reduced gradient features
as input and appropriate target. The 95 number of weights are initialized with small random values.
Sigmoidal activation function is used as the non-linearity. Training was carried out for 500 iterations
and learning curve for the same was plotted in the Fig. 9.And the same procedure is carried out for
97 reduced curvature features was plotted in Fig.10. After training, the error reduces to marginal
value and convergence is achieved. 20 data sets were chosen for the purpose of testing out of which
10 data was from odia digit 0 and 10 data was from odia digit 2. It was observed that out of 20
isolated digits only digits have been recognized correctly showing an accuracy of 80% only. The
testing results for the perceptron based scheme are shown in Table I.
Fig.4: Data Set after Normalization Fig.5: Gray scale image
Fig.6: Direction Of Gradient Fig.7: Strength Of Gradient

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
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Fig.8: Curvature
Fig.4: Training curve of Single layer perceptron for Curvature feature
Fig.5: Training curve of Single layer perceptron for Gradient feature
Table I: Testing Results
Collected odia
digits
Pattern used in
testing
Results for gradient feature Results for curvature
feature
classified Not classified classified Not classified
0 10 9 1 8 2
2 10 8 2 6 4
0 50 100 150 200 250 300 350 400 450 500
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
iteration->
mse->
0 50 100 150 200 250 300 350 400 450 500
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
iteration->
mse->

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
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Confusion matrix (CT) of gradient in testing
9 2
CT =
1 8
Confusion matrix (CT) of curvature in testing
8 4
CT =
2 6
V. CONCLUSION
In this paper we have considered the gradient features and curvature feature are used these for
the recognition of isolated handwritten odia digits. These features are then reduced using the
Principal Component Analysis. The reduced features are then separately used for training the single
layer perceptron network adopted for the classification task. The network is trained as in the single
layer perceptron network and the weights are updated using lms algorithm. Different sets of data
were prepared for training and testing. The gradient feature are tested for 10 numbers of odia digit 0
and 10 numbers of odia digit 2.Out of 10 number of odia 0 digits only 9 number of digits are
classified and out of 10 number of odia 2 digits only 8 number of digits are classified properly. So
the Acurracy of classification using gradient feature is 85%, where as in curvature feature out of 10
number of odia 0 digits only 8 number of digits are classified and out of 10 number of odia 2 digits
only 6 number of digits are classified properly. So the classification accuracy for the curvature
feature feature is 70% only.
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