RECOGNITION OF CHEISING IYEK/EEYEK-MANIPURI DIGITS USING SUPPORT VECTOR MACHINES

www.ijcsit-apm.com International Journal of Computer Science &Information Technology 1
IJCSIT, Vol. 1, Issue 2 (April 2014) e-ISSN: 1694-2329 | p-ISSN: 1694-2345
RECOGNITION OF CHEISING
IYEK/EEYEK-MANIPURI DIGITS USING
SUPPORT VECTOR MACHINES
Kansham Angphun Maring1
, Dr. Renu Dhir2
1, 2
Department of Computer Science and Engineering
National Institute of Technology, Jalandhar, Punjab, India
1
kam10maring@gmail.com, 2
dhirr@nitj.ac.in
Abstract-The development of handwriting recognition
systems began in the 1950s when there were human
operators whose job was to convert data from various
documents into electronic format, making the process quite
long and often affected by errors. Automatic text
recognition aims at limiting these errors by using image
pre-processing techniques that increases the speed and
precision to the entire recognition process. This paper
describes the recognition of Cheising Iyek-Manipuri digits;
handwritten as well as printed and comparison of
recognition accuracy using Support Vector Machines
(SVM). The paper also presents the steps starting right
from binarization of scanned images in pre-processing till
recognition of the digits using a trained model. Gabor
filter-based technique is used for feature extraction. The
experiment is carried out with image size 14x10 using
MATLAB. An overall accuracy of 89.58% and 98.45% is
achieved for handwritten and printed respectively.
Index Terms- Meitei/Meetei Mayek, Cheising
Iyek/Eeyek, Binarisation, Gabor filter, Feature Extraction,
Support Vector Machines (SVM), Support Vectors.
I. INTRODUCTION
During the last few decades, pattern recognition has got
lot of attentions from the researchers with its vast practical
applications [1] in science and technology viz; Number
Plate Recognition, Bank Cheque Processing, Postal
Automation Service, Conversation of Ancient Manuscripts
etc.
Meitei/Meetei Mayek is the script used in Manipuri
language which is primarily written and spoken by the
valley people of Manipur, India and the language so
spoken is called Meiteilon [2, 3], Meiteiron and Meithei
[4]. The exact origin of the Meetei Mayek is shrouded in
mystery by the destruction of Pre-Hindu places and the
burning of all the historical documents called the „Puya
Mei Thaba‟ [5]. The current Manipuri script is a
reconstruction of the ancient Manipuri script. Meitei
Mayek is a member of the Tibeto-Burman branch of the
Sino-Tibetan language family and is also spoken in
Bangladesh and Mynmar[5]. This script contains Iyek
Ipee/Mapung Iyek, which have 27 alphabets (18 original
plus 9 derived letters called Lom Iyek), Lonsum Iyek (8
letters), Cheitek Iyek (8 symbols), Khudam Iyek (3
symbols), Cheising Iyek (10 numeral figures) [6]. All the
Meetei Mayek numerals are derived from the embryonic
development stages of a human foetus [5]. The script was
officially approved by theGovernment of Manipur on April
22, 1980 [6] but it got its place in academics only in 2005
replacing Bengali script. This is the reason that research in
Manipuri script recognition has not yet been widely
introduced to the research community while much research
on other scripts of different languages has been published
and introduced internationally. Meetei Mayek has now
been included in the Unicode Standard, Version 5.2 which
was released on 1st October 2009 [7]. The range is ABC0-
ABFF.
The rest of the paper is organized with literature survey
in Section II followed by details of image acquisition in
Section III. Then image pre-processing steps in Section IV
and Support Vector Machine in Section V. Section VI
describes the feature extraction technique using Gabor
filter and the recognition system in Section VII.
Experimental results are shown in Section VIII and
conclusion is drawn in Section IX.
Figure 1. Cheising Iyek (Manipuri digits) and English digits
II. LITERATURE SURVEY
Digit recognition systems have been developed for
different languages in the world. In context to Indian
languages, one can find recognition of off-line handwritten
Gujarati Digits using Neural Network Approach in [8]. In
[9] handwritten Devnagari Digit Recognition:
Benchmarking on New Dataset is proposed using Neural
Network. Then in [10] and [11] Handwritten Gurmukhi
Numeral Recognition using Zone-basedHybrid Feature
Extraction Techniques and recognition system for
handwritten Assamese numerals using mathematical
morphology respectively is discussed. When it comes to
Manipuri digits, one can find limited papers. In [12]
simulation and modelling of handwritten MeiteiMayek
Digits using Neural Network Approach is discussed. They
have achieved 85% accuracy rate and mentioned that the
accuracy can be improved by using different feature sets
and classification algorithms like support vector machines
(SVM).
III. IMAGE ACQUISITION
The image acquisition for handwritten as well as printed
are described below:

International Journal of Computer Science & Information Technology 2 www.ijcsit-apm.com
A. Handwritten Manipuri digits
From different age groups and genders, around 800
samples for each digits are collected on A4 size
paper.These samples are thenscanned using a scanner at
250dpi and save in .PNG format. Figure 2 shows a sample
of the scanned copy which are then feed for pre-processing
steps.
Figure 2. A sample image of the scannedhandwritten Manipuri digits
(0-9)
B. Printed Manipuri digits
Two different fonts of Meetei/Meetei Mayek namely
“epaomayek” and “Rathayek” are used. The digits are
written on MS Word with font size of 10 points. These
digits are then captured using snipping tool and save in
.PNG format.
(a)
(b)
Figure 3. Cheising Iyek in different fonts containing digits from 0 to 9:
(a) epaomayek (b) Rathayek.
IV. IMAGE PRE-PROCESSING
The image pre-processing can significantly increase
the reliability of an optical inspection. Image pre-
processing is a series of operations performed on the
scanned input image that essentially enhances the image
rendering it suitable for segmentation. Several filter
operations which intensify or reduce certain image details
enable an easier or faster evaluation. So, pre-processing
steps differ by requirements. Image binarization
(thresholdingis the conversion ofa gray-scale image into a
binary image. Global thresholding picks one threshold
value for the entiredocument image based on an estimation
of the background level
(I) Handwritten Manipuri digit „3‟.
(II) Printed Manipuri digit „5‟.
Figure 3. Pre-processing of Manipuri digits: (a). Segmented digit (b).
Skeleton (c). Dilated Image (d). Smoothed Image.
from theintensity histogram of the image. In this paper
global thresholding technique using Otsu‟s method is
applied. Since the digits are unconnected, explicit character
segmentation technique is applied. Disk structuring
element of radius 2 is applied for dilating the images.
Before dilating, the images arethinned to infinity to find
out the skeletons of the images. This is done so that when
the images are dilated they will have equal thickness of
strokes.
V. SUPPORT VECTOR MACHINES
The idea of Support Vector Machines (SVM) were first
shared by Vapnik [13]. Support Vector Machine classifier
is an algorithm which maximises the margin between the
classes and tries to minimises the classification error. SVM
used to identify a set of linearly separable hyperplanes
which are linear functions of the feature space. Among the
separable hyperplanes only one hyperplane is chosen and
placed such that such that the distance between the classes
is maximum. SVM algorithm for linearly non-separable
classes is discussed here. Let us consider {Xi, Yi} for
i=1,2,……..,N denoting the training dataset where Yi is the
target output for training data Xi.The aim of SVM is to
maximize the objective function L(𝛼) given by:
L(𝛼)= 𝛼𝑖 -
1
2
𝛼𝑖 𝛼𝑗
𝑁
𝑗 =1
𝑁
𝑖=1 YiYjΦ(Xi)T
Φ(Xj)
(1)
subject to constraints αi
N
j=1 Yj=0, 0≤ 𝛼𝑖 ≤C; ∀ i
where C is the cost parameter that determines the cost
caused by constraint violation, 𝛼𝑖 is the hyperparameter
and Φ(·) is the feature mapping function. Asking for the
maximum-margin linear separator in equation (1) leads to
standard Quadratic Programming(QP) problems.With the
mentioned constraints, the QP solution leads to the
following classification function for support vector
machines.
Y=sgn(W. Φ(Z)+b)
Y=sgn 𝛼𝑖
𝑞
𝑖=1 𝑌𝑖 𝑋𝑖 𝑍 + 𝑏
(2)
where𝛼𝑖 is the lagrance multiplier assigned to each training
data whose value depend on the role of training the data in
the classifier system.The non-zero values of 𝛼𝑖 correspond
to the support vectors that are used to construct the
classifier in equation (2), „q‟denotes the number of support
vectors. If the feature functions Φ(·) are chosen with care
one can calculate the scalarproducts without actually
computing all features, therefore greatly reducing

Figure 4. Finding decision hyperlane function in SVM.
the computationalcomplexity. In SVM the learning
algorithms that only require dot products between the
vectors in the originalinput space, and chooses the
mapping such that these high-dimensional dot productscan
be computed within the original space, by means of a
kernel function is called “kernel trick”.
K(x, xi) = 𝜑(x). 𝜑(xi)
(3)
In this experiment, linear kernel used. Linear kernel is
given by K(x, y) = x · y; where no mapping is performed
and only the optimal hyperplane is calculated.
VI. FEATURE EXTRACTION USING GABOR FILTER
It is well known that the performance of a digit-
recognition system depends significantly on the features
used. Selection of a feature extraction method is probably
the single most important factor in achieving high
recognition rate in digit recognition. The extracted features
should be able to identify each digits uniquely and there
should be large variations in the features for different
digits. Gabor filters [14] have been used extensively in
computer vision [15] and texture analysis for their
excellent properties: optimal joint spatial/spatial frequency
localization and ability to simulate the receptive fields of
simple cells in the visual cortex [16]. These characteristics
suggest that the Gabor-filter based feature seems to be
similar to features extracted by humans and thus, may be
effective in classifying digits. Shustorovich [17]applied
Gabor filters to handwritten numerals and showed that
Gabor-filter-based features are superior to pixel-based
features.A two-dimensional Gabor filter is defined by:
f(x,y,θk,λ,σx,σy)=exp −
1
2
𝑅1
2
𝜎 𝑥
2 +
𝑅2
2
𝜎 𝑦
2 ∗ 𝑒𝑥𝑝 𝑖
2𝜋𝑅1
𝜆
(4)
whereR1= x cos 𝜃 𝑘+ ysin 𝜃 𝑘 and R2= -x sin 𝜃 𝑘 + ycos 𝜃 𝑘.
λand𝜃 𝑘are the wavelength and orientation of the sinusoidal
plane wave respectively. The values of R1 andR2 allows the
Gabor to stretch in any direction defined by 𝜃. In order to
prevent the occurrence of undesired effects at the image
borders, the wavelength value should be smaller than on
fifthof the input image size.σxandσy
are the standard deviations of the Gaussian envelope along
the x-axis and y-axis directions respectively which gives
the spread in the axes of two-dimensional Gaussian. A
rotation of the x-y plane by an angle 𝜃 𝑘will result in a
Gabor filter at orientation 𝜃 𝑘. The value of 𝜃 𝑘is given by
𝜃 𝑘 = π (k-1) / m, k = 1,……,m, where m denotes the
number of orientations. For instance when m = 4, the
orientation θused are: 0°
, 45°
, 90°
and 135°
and are shown
in figure 5 (a). A set of Gabor filters with 5 spatial
frequencies and 8 distinct orientations making 40 different
Gabor filters that is used in the experiment is presented in
Figure. 6.
The Gabor feature can be viewed as the response of the
Gabor filter located at a sampling point. The response is
obtained by convolving the filter with an image. Gabor
filters extract the orientation-dependent frequency
contents, i.e., edge like features, from as small an area as
possible. Figure 5 shows how Gabor features are extracted
using by varying orientations, with the value of λ fixed.For
each sampling point, m Gabor features can be obtained for
morientations. The feature vector is a 2-D matrix with
features as rows and levels of the digits in first column.
When Gabor filters are applied to each pixels of the image,
the dimension of the filtered vector can be very large
(proportional to the image dimension). So, it will lead to
expensive computation and storage cost. To alleviate such
problem and make the algorithm robust, Gabor features are
collected at regular intervals of pixels. This technique is
very useful for face recognition system where Gabor
features are obtained only at the fiducial points [18].
(a)
(b)
Figure 5. Extracting Gabor features for handwritten Manipuri digit: (a) 6
(six) and (b) 7 (seven).

Real parts of filters Magnitudes of filters
Figure 6. 40 Gabor filters with 5 spatial frequencies and 8 different
orientations used to extract features of handwritten as well as printed
Manipuri digits.
In order to recognize handwritten Chinese characters, eight
orientations were needed [19]. But this is not true for all
the scripts due to the differences in required number of
strokes in writing a character, structure of the characters
and writing styles.
VII. RECOGNITION OF CHEISING IYEK/EEYEK
For recognition of the Manipuri digits, SVM classifier is
used. As we all know that SVM is binary classifier but it
can be used as multiclass classifier in different ways. In
this paper all-at-once multiclass classifier support vector
machines is used. In all-at-once support vector machines
for an n-class problem, the decision function for class iis
defined by:
Di(x) = 𝑊𝑖
𝑇
Φ(x) + bi ,
(5)
whereWiis the weight vector for class i in the feature space,
Φ(x)is the mapping function, and bi is the bias term. For
class i data x to be correctly classified, Di(x) needs to be
the largest among Dj(x) (j = 1,………..,n), namely, the
following inequalities must hold:
𝑊𝑖
𝑇
Φ(x) + bi >𝑊𝑗
𝑇
Φ(x) + bj for j≠i,j= 1,………..,n.
(6)
All the digits are labelled with labels 0, 1, 2,…..,9. The
feature vector for training and testing are created separately
using the feature extraction technique discussed in section
VI. For handwritten 600 and 120 samples for each digit are
selected for training and testing respectively. In case of
printed, 1000 samples are taken for training and 200
samples for testing. Approximately same number of
datasets from the two fonts are used for training and
testing. A classifier model is created using linear kernel
and feature vector of training dataset as input. The testing
feature vector is applied to the trained model and
recognition is done finding out overall percentage
accuracy, predicted labels, andprobability values. From
these values experimental results are drawn.
VIII. EXPERIMENTAL RESULTS
The experiment was carried for image size 14x10. When
a set of Gabor filters with 5 spatial frequencies and 8
distinct orientations (40 Gabor filters), the accuracy is at its
highest point. Varying the number of filters, accuracies are
compared. The experimental results are sectioned as
follows:
A. Handwritten Manipuri digits
The Table I shows the confusion matrix. The diagonal
element shows the number of digits that are correctly
recognized. While the values in the rest of the rows shows
the number of digits that were misclassified against the
digit in column number 1. Consider digit 9(nine), 82 digits
out of 120 were correctly recognized and misclassified
seven times as 2, twice as 3, sixteen times as 6, once as 7,
and twelve times as 8.
TABLE I
CONFUSION MATRIX OF HANDWRITTEN MANIPURI DIGITS 0(ZERO)-
9(NINE).
0 1 2 3 4 5 6 7 8 9
0 118 2
1 117 1 2
2 1 98 4 1 8 8
3 7 112 1
4 2 1 116 1
5 113 2 5
6 3 2 4 103 8
7 3 116 1
8 2 1 6 7 100 4
9 7 2 16 1 12 82
TABLE II
PERCENTAGE ACCURACY OF RECOGNITION OF HANDWRITTEN MANIPURI
DIGITS 0(ZERO)-9(NINE).
Digits Attempts CR FR Accuracy (%)
0 120 118 2 98.33
1 120 117 3 97.50
2 120 98 22 81.67
3 120 112 8 93.33
4 120 116 4 96.67
5 120 113 7 94.16

6 120 103 17 85.83
7 120 116 4 96.66
8 120 100 20 83.33
9 120 82 38 68.33
Table II shows the performance of the SVM model for
all the testing digits. For each digits, 120 images are given
for recognition and result is shown below along with
accuracy for each digits. FR means false recognition and
CR means correct recognition. From the table it is known
that digit 9 has the lowest accuracy, 68.33% with 35 false
recognitions and recognizing 14 times as 6. This drags
down the overall accuracy of the recognition system to
89.58%.
The graphical representation between correct
classification (CR) and false recognition (FR) is shown in
figure 7. When more Gabor filters are applied beyond
require, the performance improves little at the cost of
dramatically increasing computations. This is shown Table
III below: With the increase in number of Gabor filters
used the accuracy increases a little bit but at some point it
saturates and drops down. This data may be different for
different scripts because of difference in their strokes and
styles.
Figure 7. Graphical representation of CR and FR for handwritten
Manipuri digits.
TABLE III
EFFECT OF NUMBER OF GABOR FILTERS IN ACCURACY AND EXECUTION
TIME.
No. of
Gabor
Filters
Execution time (Min.) Accuracy of the
recognition
system (%)
Training(600
samples)
Testing(120
samples)
20 28.21 5.80 87.16
30 42.72 8.17 88.91
40 53.04 12.62 89.58
50 73.62 15.75 89.25
B. Printed Manipuri digits
The experimental results for printed Manipuri digits are
analysed in figure 8. For each digits 1000 samples, mixed
digits of both fonts are taken for training and 200 samples
are considered for testing. Each printed digits has higher
accuracy than its counter digits in the handwritten. Digits
„0‟, „1‟, „4‟, and„7‟has 100% accuracy rates. As in
handwritten, digit „9‟got lowest accuracy at 89.5 %.
Figure 8. Graphical representation of CR and FR for printed Manipuri
digits.
C. Comparison between Handwritten and Printed
The accuracy for handwritten and printed digits are
compared in the figure 9. Digit „0‟ has comparatively
equal accuracy for handwritten as well as printed. Huge
increased in recognition rate is observed for digits
„2‟(two), „6‟(six), „8‟(eight) and „9‟(nine).
Figure 9. Accuracy comparison between handwritten and printed
Manipuri digits.
IX. CONCLUSIONS
In this paper Gabor filter-based feature extraction method
is used for the recognition of handwritten Manipuri digits.
The overall performance of the system for handwritten is
89.58% and that of printed is 98.45%. Some of the digits
are very much similar to each other. The presence of blob
and little change in stroke angle differentiates them. So, the
recognition system can be improved by fusing blob
detection technique to the proposed system. As per the
printed digits is concerned, taking more fonts and trying to
classify the digits of different fonts which are not in
training will be interesting. In future Pairwise Support
Vector Machines will be used for same feature extraction
technique. To avoid unclassifiable regions in Pairwise

Support Vector Machine, Decision-Tree-Based Support
Vector Machine may be considered.
REFERENCES
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NationalLaboratories Proposal), 1998.
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[4]. T.C.Hodson, The Meitheis, Low price publications, Delhi,1908.
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Diploma Project for Graduate Diploma Programme in Design,
National Institute of Design, Ahmedabad, 2006.
[6]. Government of Manipur, APPROVED MEITEI/MEETEI MAYEK,
Approved script vide " Manipur Gazzette No 33 dated April 22,
1980 Annexure 1 to 5 (1/2/78-SS/E)".
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Singh, A.Suresh Singh, H.James, Simulation and Modeling of
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AUTHORS
First Author – Kansham Angphun
Maring: Received his bachelor‟s degree
in Computer Science and Engineering
from North Eastern Regional Institute
of Science and Technology (NERIST),
Arunachal Pradesh, India. He is
currently pursuing his master‟s degree
in Computer Science and Engineering at Dr. B.R
Ambedkar National Institute of Technology, Jalandhar,
India. His area of interest are Digital Image Processing,
Natural Language Processing, Computer Networks and
Wireless Sensor Networks.
Second Author–Dr. RenuDhir:
Associate Professor in Department of
Computer Science and Engineering at Dr.
B.R Ambedkar National Institute of
Technology, Jalandhar, India. Her area of
interest includes Image Processing,
Pattern Recognition, Natural Language
Processing and Machine Learning.
.

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RECOGNITION OF CHEISING IYEK/EEYEK-MANIPURI DIGITS USING SUPPORT VECTOR MACHINES