International Journal of Application or Innovation in Engineering & Management (IJAIEM)
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Volume 3, Issue 6, June 2014
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Neuro-Fuzzy Model Based Classification of
Handwritten Hindi Modifiers
Gunjan Singh1, Dr. Avinash Pokhriyal2, Prof. Sushma Lehri3
1
Assistant Professor, R.B.S.M.T.C.Agra.
Associate Professor, R.B.S.M.T.C., Agra
3
Ex-Director, I.E.T., Dr. B.R. Ambedkar University, Agra
2
Abstract
Automatic character recognition is one of the most important and interesting topic of pattern recognition field. A lot of work has
been done in the area of machine printed character recognition, but recognition of handwritten characters is comparatively
difficult and still a subject of active research. In this paper, we present a neuro-fuzzy system for accurate and adequate
classification of handwritten Hindi modifiers or matras. Total 1000 handwritten samples of 10 modifiers are collected from 10
different people. System works in six stages—data collection & scanning, preprocessing, normalization, feature extraction, fuzzy
rule set creation and classification. System works on fuzzy information and has a layered architecture—input layer, hidden layers
and output layer. Function of first hidden or degree of membership (DOM) layer is to generate the degree of membership for all
input-output pattern pairs. Generated degree of membership is then used to train the system using backpropagation algorithm to
perform final classification of rules and to determine the membership function of generated output for each class. The
membership function is used to determine the classified output pattern corresponding to the input pattern. Feature extraction is
done by convoluting a 3X3 mask on pre-processed modifier image. At the end, a comparative study of proposed system with
existing systems is also done to evaluate the performance. The proposed system has been implemented in MATLAB 2009
environment.
Keywords: Neuro-fuzzy model, classification, fuzzy if-then rules, membership function, backpropagation learning
algorithm.
1. INTRODUCTION
Handwritten character recognition is a wide area of pattern recognition field which covers all sorts of character
recognition in various application domains such as handwriting recognition, signature recognition, postal address
processing etc[1]. This area is becoming more and more important as the amount of data captured & stored (either online
or offline) is increasing rapidly with advancements in computer technology, and due to huge amount of data manual
identification (such as searching, retrieving, processing and maintaining) of documents’ text is becoming tedious and
time consuming process. A lot of work has been done in the area of machine recognition of handwritten characters, still
problem cannot be considered as solved. One of the major problems is that handwritten characters are vague, nonuniform in nature and may have some level of fuzziness in their appearance.
Artificial neural network and fuzzy set theory has been applied effectively either individually or in combined form to
solve automatic character recognition problem since long. An artificial neural network (ANN) is an information
processing system in which a large number of processing elements, called neurons, are arranged in layers and each
neuron is connected to several other neurons by means of directed communication links. Each link is assigned a weight
[2]. A neural network learns through training. Learning or training is done by adjusting weights between layers. Learning
can be supervised (learning through teacher), unsupervised (learning without teacher) or reinforcement learning. In
supervised learning, network is trained to achieve the target output vector by adjusting weights according to a learning
law [3]. In this paper, we used backpropagation learning law to train the proposed network [4]-[8].
Human beings are capable to deal with real world information that is fuzzy, partial and approximate because our
experience provides us a way to put together and analyze these pieces of information into a structured way. Fuzzy logic is
an effective tool to describe human knowledge involving vague or fuzzy uncertainties. The concept of fuzzy set theory was
given by Lofti A. Zadeh in 1965. According to Zadeh [9], fuzzy logic is a method to solve and model the uncertainties
associated with human thinking and reasoning processes. The concept of fuzzy set allows partial membership so a fuzzy
set may have smooth boundaries. Membership of an element of fuzzy set is expressed by the degree of compatibility of the
element with the definition of that set. Membership is expressed between number 0 (complete non-membership) and
1(complete membership). Value in-between 0 and 1 represents partial membership [10]. A fuzzy set A in the universe of
discourse U (into the interval [0,1]) can be defined as a set of ordered pairs and is given by:
à = { (x, µ à (xi) ) | x ∈U}
where µ à (x) represents degree of membership of x in à and called membership function of Ã. Fuzzy rules is by far the
most used and successful method of fuzzy logic theory to represent inexact and ambiguous data.
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Strength of neural network is its learning and storing capability, while fuzzy logic handles the complexity of fuzzy data
very well by expressing it in the form of IF-THEN rules [11]. So, a neural-fuzzy system incorporates the features of
learning through logic and hence enhances efficiency of the system. In this paper, we presented a novel neural-fuzzy
model to solve classification problem of handwritten Hindi curve script modifiers.
In neuro-fuzzy aproach, fuzzy and neural networks are used independently, where each one perform its functions, and
serve the system by incorporating and complementing each other in order to accomplish the desired task. System works
by interpreting the fuzzy rules in terms of the neural network. Fuzzy sets are taken as weights, while fuzzy rules, input
and output variables are taken as neurons[12]. System learns by applying a learning algorithm as in a neural network, and
accordingly changes in weights taken place. As a result, change in the architecture may be possible. So networks learns in
the form of fuzzy rules. It can be said that, in neuro-fuzzy approach, neural network is used to—(i) process fuzzy
inforamation, and (ii) implement the fuzzy system.
This paper is organized in six sections. In section 1, a brief introduction of automatic character recognition, artificial
neural networks, fuzzy set theory and neuro-fuzzy system is given. Section 2 throws some light on features of Hindi
language. Section 3 presents the proposed recognition system. In Section 4 experimental results are presented. Section 5
and 6 are devoted to comparative study & conclusion respectively.
2. HINDI LANGUAGE
Hindi is one of the official languages of India. It is an Indo-Aryan language. It is the world’s third most commonly used
language after Chinese and English and has approximately 500 million speakers all over the world. It is based on
Devnagari script. The basic character set has 39 characters : 13 vowels (SWARS) and 33 consonants (VYANJANS).
Language also has 10 basic modifiers (figure 1) . Characters may also have half form, composite form and compound
form (figure 2).
(a)
(b)
Figure 1 (a) Basic character set of Hindi language and (b) set of modifiers
(a)
(b)
(c)
Figure 2 (a) Half characters, (b) composite characters, and (c) compound characters
Now-a-days Hindi language is used worldwide in many application areas such as banking, engineering, medical etc. Due
to its increasing popularity, automatic Hindi language recognition systems have now become the need of time. Research
in this area started in early 1970s. Till than a number of methods and techniques have been proposed [13]-[19].
3. PROPOSED SYSTEM
Proposed system works in 6 stages—data collection & scanning, preprocessing, normalization, feature extraction, fuzzy
rule set creation and classification. Flow process is shown below--
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Collect handwritten data of 10
modifiers on paper
ISSN 2319 - 4847
Recognized modifier
Defuzzify output data
Scan data to convert data to
gray scale images
Create and train fuzzy neural
network
Apply median filtering to noise
reduction
Create a set of fuzzy rules for
each modifier by using
extracted feature values
Apply Global thresholding to
perform binarization
Normalized
modifier image
Normalize images to the size of
7x7
Extract values of projection
profile, normalized chain code
& pixel ratio features for each
modifier image
Figure 3 Flow process of proposed system
3.1 DATA COLLECTION & SCANNING
Handwritten modifier data samples are collected on paper and scanned through an optical scanner.
3.2 PRE-PROCESSING
During pre-processing following operations are applied on data to make it noise free and suitable for further processing –
a.
Median filtering[20] to reduce noise and false points,
b.
Global thresholding [20] for binarization i.e. to convert gray scale images to binary form.
3.3 NORMALIZATION
Binary images are normalized to 7x7 size using MATLAB function imresize( )[21].
3.4 FEATURE EXTRACTION
Hindi language is curve based, i.e. each character and modifier is made up of one or more curves, lines and/or loop. In
proposed system, feature extraction is done by extracting values of following features for each modifier—
i. Horizontal projection profile,
ii. Curves’ normalized chain code , and
iii. Pixel ratio.
Header line or SHIROREKHA plays an important role in formation of Hindi text. Here we use the header line feature for
classifying modifiers into different categories. To extract the value of header line feature we apply horizontal projection
profile method on normalized modifier image. Then image is thinned by applying [22] and normalized curves’ chain
coding scheme and pixel ratio is calculated for each classified modifier to perform final identification & recognition.
A. HORIZONTAL PROJECTION PROFILE
A projection profile is a histogram of the number of black pixel values accumulated along parallel lines taken through the
document [23]. Projection profile methods have been widely and successfully used for skew detection & correction, text
line segmentation, word segmentation, page layout segmentation etc. Horizontal projection profile is the method to find
the number of character or foreground (black) pixel values collected along rows. Procedure to find horizontal projection
profile is given below-
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HPP=Procedure Horizontal_Profile(I: grayscale image)
Read image I
Convert I to binary image B
Resize B to the size 64x64
h_proj  zeros
for i = 1 to 64
sum  0
for j=1 to 64
if I(i,j)==0 //if black pixel found
sum  sum+1
endif
endfor
h_proj(i)  sum
endfor
return h_proj
On the basis of position of highest profile on x-axis in the range of 0-60, we divided modifiers into three classes—
Class
Position of highest profile in
HPP (value at x-axis)
Modifier
(BADA AA),
1
Left End (below 20)
(CHOTA U),
(BADA U) and
(RI)
(CHOTI E),
2
Middle (20-40)
(BADI E),
(CHOTA AU)
(BADA AU)
3
Right End (Above 40)
(CHOTA AE)
(BADA AE)
Table1 Classification of modifiers into three classes using horizontal projection profile
B. CURVES’ NORMALIZED CHAIN CODE
Curves’ normalized chain codes are obtained by following one of the oldest techniques of feature extraction- the chain
coding scheme, in computer vision. It was introduced in 1960s. In this scheme, a set of pixels in contour of a shape of an
object or character is translated into a set of connections between them. Contour is traced by starting from one pixel and
determining one of the 8 adjacent directions in which the next pixel is to be found. Chain code is formed by concatenating
the number that designates the direction of the next neighboring pixel as shown in figure. Process is repeated till the
starting pixel is reached [24].
Figure 4 Numbering of neighboring pixels in chain coding
Proposed method is based on chain coding scheme with a slight difference. In this method, starting point and ending
pixels are different. Values are obtained in the range of 0.25 -0.55. Step-by-step method is given in procedure
CHN_CODE_FEATURE_EXTR ().
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Following variables are used—
CP : Current pixel
SUM : Stores sum of chain codes of all black pixels in the curve. Initial value is set to zero.
I
: Counter variable. Stores number of black pixels in the curve.
NCC=Procedure CHN_CODE_FEATURE_EXTR (I: thinned image)
Step1. Read image I
Step2. Convolute 3x3 mask on modifier image row wise from lower left end to upper
right end. P is the centre pixel under the mask.
Step3. if pixel P is a black pixel
if P is an end point, then
P  CP
endif
endif
Step4.Search for neighboring pixels in 8-way connectivity and start moving along
the curve clockwise as follows—
(a) First searching for P2, P3 or P4 neighboring pixel, then P0, P1, P5,
P6 or P7 pixels.
if pixel P is a modifier pixel, then
(i) I  I+1
(ii) SUM  SUM+N
(where N is assigned the number
according to the direction given in
figure 5).
(iii) P  CP and repeat.
endif
(b) if pixel is a header line pixel
if pixel is a junction point or end point
if all pixels in the curve are searched at least once
then stop
else go to step (a)
endif
endif
endif
Step5. Calculate normalized chain code by the following equation—
NC  N
return NC
Junction point
E.g.
Chain code is – 2 2 2 2 2 2
SUM = 12, N= 6
NC= 0.5
CP
Figure 5 Illustration of normalized chain coding procedure of proposed method
C. PIXEL RATIO
Third feature is the ratio of black pixels to total number of pixels in the image. Values are obtained in the range of 0.25 0.55.
PR=Procedure PIXEL_RATIO(I: thinned image, m: number of rows in I, n: number
of columns in I)
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TP
NB
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:
:
Total number of pixels in the image, and
Number of black pixels in the image.
for i: 1 to m
for j: 1 to n
PR(i,j)  NB(i,j)/ TP(i,j)
endfor
endfor
return PR
Finally procedure FEATURE_EXT() is called—
[HPP,NCC,PR]=
{
HPP = call
NCC = call
PR = call
}
procedure FEATURE_EXT(mi: modifier image)
procedure HORIZONTAL_PROFILE(mi)
procedure CHN_CODE_FEATURE_EXTR (mi)
procedure PIXEL_RATIO(mi)
3.5 FUZZY RULE SET CREATION
Fuzzy rule set is created by converting the crisp feature values to fuzzy form and forming fuzzy IF-THEN rules.
Linguistic variables for inputs and outputs are shown in the table 2.
Table 2: Linguistic variables corresponding to input variables
Symbol
Linguistic Variable
Range
LE (left end)
0-20
Horizontal Projection Profile (HPP)
CL
MD (middle)
20-40
RE (right end)
40-60
VL (very low)
0.25-0.30
L (low)
0.30-0.30
Curves’ normalized chain code
NC
BA (below average)
0.35-0.40
(NCC)
A (average)
0.40-0.45
H (high)
0.45-0.50
VH (very high)
0.50-0.55
VL (very low)
0.25-0.30
L (low)
0.30-0.30
Pixel ratio(PR)
PR
BA (below average)
0.35-0.40
A (average)
0.40-0.45
H (high)
0.45-0.50
VH (very high)
0.50-0.55
Feature
3.5
Total 33 fuzzy rules are created to link these 15 input membership values to 10 output membership values as follows--
R1: IF CL is LE and NC is A and PR is VL THEN MF is
(BADA AA).
R2: IF CL is MD and NC is H and PR is BA THEN MF is
(CHOTI E).
R3: IF CL is MD and NC is H and PR is A THEN MF is
(CHOTI E).
R4: IF CL is MD and NC is VH and PR is BA THEN MF is
CHOTI E).
R5: IF CL is MD and NC is VH and PR is A THEN MF is
(CHOTI E).
R6: IF CL is MD and NC is VL and PR is BA THEN MF is
R7: IF CL is MD and NC is VL and PR is A THEN MF is
(BADI E).
(BADI E).
R8: IF CL is LE and NC is VL and PR is A THEN MF is
(CHOTA U).
R9: IF CL is LE and NC is VL and PR is H THEN MF is
(CHOTA U).
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R10:IF CL is LE and NC is BA and PR is A THEN MF is
CHOTA U).
R11:IF CL is LE and NC is BA and PR is H THEN MF is
(CHOTA U).
R12:IF CL is LE and NC is VL and PR is A THEN MF is
(BADA U).
R13:IF CL is LE and NC is VL and PR is VH THEN MF is
R14:IF CL is LE and NC is L and PR is A THEN MF is
(BADA U).
(BADA U).
R15:IF CL is LE and NC is L and PR is VH THEN MF is
(BADA U).
R16:IF CL is LE and NC is BA and PR is L THEN MF is
(RI).
R17:IF CL is LE and NC is BA and PR is BA THEN MF is
(RI).
R18:IF CL is LE and NC is A and PR is L THEN MF is
R19:IF CL is LE and NC is A and PR is BA THEN MF is
R20:IF CL is RE and NC is BA and PR is L THEN MF is
(RI).
(RI).
(CHOTA AE).
R21:IF CL is RE and NC is BA and PR is BA THEN MF is
(CHOTA AE).
R22:IF CL is RE and NC is VL and PR is A THEN MF is
(BADA AE).
R23:IF CL is RE and NC is VL and PR is H THEN MF is
(BADA AE).
R24:IF CL is RE and NC is BA and PR is A THEN MF is
(BADA AE).
R25:IF CL is RE and NC is BA and PR is H THEN MF is
(BADA AE).
R26:IF CL is MD and NC is H and PR is BA THEN MF is
(CHOTA AU).
R27:IF CL is MD and NC is H and PR is A THEN MF is
R28:IF CL is MD and NC is VH and PR is BA THEN MF is
R29:IF CL is MD and NC is VH and PR is A THEN MF is
R30:IF CL is MD and NC is VL and PR is BA THEN MF is
(CHOTA AU).
(CHOTA AU).
(CHOTA AU).
(BADA AU).
R31:IF CL is MD and NC is VL and PR is A THEN MF is
(BADA AU).
R32:IF CL is MD and NC is L and PR is BA THEN MF is
(BADA AU).
R33:IF CL is MD and NC is L and PR is A THEN MF is
(BADA AU).
Figure 6 Set of fuzzy rules
3.6 CREATION OF PROPOSED NEURO-FUZZY SYSTEM
Adaptive Neuro-fuzzy Inference System (ANFIS) is one of the most widely used way of combining both fuzzy set theory
and neural networks. Proposed ANFIS consists of six following layers —
i. Input node layer – every node in this layer corresponds to input feature used for recognition.
ii. Degree of membership layer – nodes in this layer defines the degree of membership of each lingusitic variable
corresponding to each input node.
iii. Rule node layer – nodes are used to show the IF part of fuzzy rules. Nodes are also known as antecedent nodes. Each
node output represents the firing strength of a rule.
iv. Normalization layer – also termed as consequent node layer. Each node calculates the ration of the rule’s firing
strength to the sum of all rule’s firing strength. Outputs are called normalized firing strength.
v. Defuzzification layer – nodes in this layer are adaptive.
vi. Output layer – it contains a single aggregate neuron.
4. EXPERIMENTAL RESULTS
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Results of experiment during various stages are as follows—
Data collection and scanning—
For the experiment 1000 (100 for each) samples of 10 modifiers are collcted from 10 different people of different age
groups. A sample of data set is shown below—
Figure 7 Sample data set of modifiers
Results of extraction of horizontal projection profile for each modifier are :
Modifier
Scanned image
sample 1
Horizontal Projection
profile of sample 1
Scanned image
sample 2
50
Horizontal Projection
profile of sample 2
40
45
35
40
30
35
25
30
25
20
20
15
15
10
10
5
5
0
0
0
10
20
30
40
50
60
70
0
10
20
30
40
50
60
70
0
10
20
30
40
50
60
70
0
10
20
30
40
50
60
70
0
10
20
30
40
50
60
70
50
60
45
50
40
35
40
30
30
25
20
20
15
10
10
5
0
0
10
20
30
40
50
60
70
70
0
60
60
50
50
40
40
30
30
20
20
10
10
0
0
10
20
30
40
50
60
70
0
50
70
45
60
40
35
50
30
40
25
20
30
15
20
10
5
10
0
0
10
20
30
40
50
60
70
0
60
60
50
50
40
40
30
30
20
20
10
0
10
0
10
20
30
40
50
60
70
0
0
10
50
40
45
35
40
30
20
30
40
50
60
70
35
25
30
20
25
15
20
10
15
5
10
0
5
0
0
10
20
30
40
50
60
0
10
20
30
40
50
60
70
0
10
20
30
40
50
60
70
0
10
20
30
40
50
60
70
0
10
20
30
40
50
60
70
0
10
20
30
40
50
60
70
70
70
50
45
60
40
50
35
30
40
25
30
20
15
20
10
10
5
0
0
10
20
30
40
50
60
70
40
0
70
35
60
30
50
25
40
20
15
30
10
20
5
10
0
0
10
20
30
40
50
60
70
0
60
60
50
50
40
40
30
30
20
20
10
0
10
0
10
20
30
40
50
60
70
0
60
40
35
50
30
40
25
20
30
15
20
10
10
5
0
0
10
20
30
40
50
60
70
0
Figure 8 Horizontal projection profiles of selected modifiers
System is designed with following parameters—
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Number of nodes in input layer
Number of nodes in degree of memership layer
Number of nodes rule layer
Number of nodes in normalization layer
Number of nodes in defuzzification layer
:
:
:
:
:
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3
15
15
33
10
Structure, FIS editor, plots of membership functions of input variables, rule editor, surface viewer and rule viewer is
shown in figure 9 (a)-(e) --
(a)
(b)
(c)
(d)
(e)
Figure 9 (a) Structure of the proposed system, (b) FIS editor (c) Plot of membership function for input variable CL, (d)
Plot of membership function for input variable NC and (e) Plot of membership function for input variable PR
Rule editor, suface viewer and rule viewer of the proposed system are shown in figure 10 (a)-(c).
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(a)
(b)
(c)
Figure 10 (a) Rule editor (b) Surface viewer and (c) Rule viewer
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To train the neural network in supervised mode, we give linguistic values to each modifier so that the network could train
itself to achieve that linguistic value after training number of patterns (of modifiers)Table 3 Linguistic variables corresponding to output variables
Modifier
Linguistic Value
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
So inputs and outputs are denoted as:
CLASS
: μA(CL) where A = { LE,MD,RE}
Normalized chain code
: μB(NC) where B = {VL,L,BA,A,H,VH}
Pixel ratio
: μc(PR) where C = {VL,L,BA,A,H,VH}
Modifier
: μD(MODIFIER) where D = {0.1,0.2,0.3,0.4,0.5,0.6, 0.7,0.8,0.9,1.0}
A glimpse of the set of 1000 values is shown below, that are used to train the system-CL
NC
PR
MODIFIER
12
0.45
0.27
0.1
20
0.476
0.288
0.1
5
0.486
0.276
0.1
27
0.459
0.398
0.2
33
0.515
0.365
0.2
58
0.399
0.49
1
56
0.376
0.423
0.8
26
0.299
0.367
0.3
25
0.267
0.442
0.3
34
0.325
0.444
0.9
22
0.255
0.42
0.4
2
0.468
0.213
0.1
23
0.283
0.354
0.3
9
0.313
0.503
0.5
8
0.325
0.515
0.5
27
0.279
0.434
0.3
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29
0.285
0.45
0.3
5
0.476
0.299
0.1
30
0.354
0.415
0.9
31
0.324
0.456
1
50
0.389
0.343
0.7
11
0.3
0.5
0.5
51
0.396
0.367
0.7
56
0.266
0.432
0.8
14
0.458
0.278
0.1
22
0.523
0.385
0.2
12
0.401
0.376
0.6
26
0.525
0.367
0.2
53
0.454
0.4
0.8
30
0.467
0.435
0.2
17
0.261
0.432
0.4
45
0.355
0.365
0.7
18
0.267
0.423
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7
0.272
0.514
0.5
11
0.445
0.288
0.1
10
0.322
0.526
0.5
58
0.286
0.426
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6
0.272
0.52
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11
0.356
0.356
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32
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0.463
1
13
0.365
0.383
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14
0.374
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15
0.406
0.412
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45
0.356
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12
0.343
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48
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0.384
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11
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0.301
0.429
Figure 11 A Set of 50 values fed to the system
0.4
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After training of the network, recognition rate of each modifier is obtained. Graphical representation of recognition rate
of each modifier is shown in figure --
Figure 12 Graphical representation of recognition rate of each modifier
5. COMPARATIVE STUDY
A comparative study for the proposed work has been presented in which the proposed work is compared with methods of
Sheth et al.[25], Holambe & Thool[26], Kumar & Ravichandran[27] and Nirve & Sable[28]. In this comparative study,
we compared proposed system with other neuro-fuzzy systems designed for handwritten characters as well as with
systems designed for handwritten Devnagari modifiers.
Table 4 Comparison results of proposed method with other methods
Technique
Dataset
Recognition rate
Template matching and
Gujrathi script modifiers
64%
fringe distance classifier
Sheth et al. [26]
Neuro-fuzzy System
Overlapped English
85.92%
Characters
Holambe & Thool[27]
Radial Basis Function
Devnagari Consonants
90%
Network
with modifiers
Holambe & Thool [27]
Hidden Markov Model
Devnagari Consonants
91%
with modifiers
SureshKumar &
Fuzzy Neural Network
Handwritten Tamil
90%
Ravichandran [28]
Characters
Nirve & Sable[29]
ANFIS
Devnagari characters
95%
Proposed method
ANFIS
Devnagari modifiers
97.6%
Author
Shah & Shrama [25]
Figure 13 Graphical representation of comparative study with other methods
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CONCLUSION
In this paper, a neuro-fuzzy approach (ANFIS) based classification & recognition system for handwritten Hindi modifiers
is presented. Proposed system is also compared with the methods given in [25]-[28] and comparative study shows that the
proposed system provides better results than other methods. In this paper, an efficient feature extraction algorithm, which
works well with handwritten curve script, is also presented.
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Recognition”, In Proc. of Int. Conf. on Cognition and recognition, pp. 514-520,2005.
[17] Hanmandlu H., Murthy O.V.R. , Madasu V.K. , “ Fuzzy Model Based recognition of Handwritten Hindi Characters”,
IEEE Computer Society , Digital Image Computing Techniques and Applications, 2007.
[18] Singh G. and Lehri S., “ Recognition of Handwritten Hindi Characters Using Backpropagation Neural Network”,
International Journal of Computer Science and Information Technologies, vol. 3(4), 4892-4895,2012.
[19] Singh G., Pokhriyal A. and Lehri S., “ Fuzzy Rule Based Classification and Recognition of Handwritten Hindi Curve
Script”, International Journal of Computer Engineering & Technology, vol. 4, issue 1, pp. 337-357, 2013.
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[29] Nirve S.A. and Sable G.S., “Optical character Recognition for Printed Text in Devanagari Using ANFIS”,
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AUTHOR
Gunjan Singh received the degree in Master of Computer Applications from Dr. B.R. Ambedkar University, Agra, UP, India in
2002. She is doing her Ph.D. in Computer Science. Currently, she is assistant professor in MCA department at R.B.S.M.T.C., Agra,
UP, India. She has published many papers in several reputed journals such as IJCSIT, IJCET, etc.
Her research interests include character recognition, pattern recognition, soft computing, etc.
Dr. Avinash Pokhriyal received the degree in Master of Computer Applications from Dr. B.R. Ambedkar University,
Agra, UP, India in 1997. He received the Ph.D. degree in Computer Science on the topic Minutiae based Automatic
Fingerprint Recognition. Currently, he is Associate professor in MCA department at R.B.S.M.T.C., Agra, UP, India. He
has published and presented several papers in national and international journals and conferences. He has reviewed many
papers in several reputed journals such as IJCSNS, JISE, JITE, WASET, IJCSES, etc. He is a member of Editorial board
of reputed international journals like IJoFCS, IJCA, IJBB, IACSIT, IJPS, etc. His research interests include biometric
based recognition, image processing,pattern recognition, soft computing, etc.
Prof. Sushma Lehri received the M.Tech. degree in Computer Science from the DOEACC, India in 2000. She received the Ph.D.
degree in Solid State Physics from the Dr. B.R.Ambedkar University, Agra, UP, India in 1980. She has also been the Director of
I.E.T., Dr. B. R. Ambedkar University. She has many publications in national & international journals. She has many awards such as
research fellowship and postdoctoral fellowship by university grant commission (UGC), New Delhi to her name. Her research interests
include image processing, network security, and solid state physics.
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