Challenges in indian currency denomination recognition & authentication

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 11 | Nov-2014, Available @ http://www.ijret.org 477
CHALLENGES IN INDIAN CURRENCY DENOMINATION
RECOGNITION & AUTHENTICATION
Syed Ejaz Ali1
, MrigankaGogoi2
, Subra Mukherjee3
1
PG student, Dept. of ECE, School of Technology, Assam Don Bosco University, Guwahati, India
2
Asst. Professor, Dept. of ECE, School of Technology, Assam Don Bosco University, Guwahati, India
3
Asst. Professor, Dept. of ECE, School of Technology, Assam Don Bosco University, Guwahati, India
Abstract
Automatic currency recognition and authentication has become an impending challenge today particularly because of the
prevailing fraudulent activities as it hampers our economy. According to the RBI report 435,607 fake notes has been detected in
year 2010-2011 and the number is only increasing with technological advancements in the field of printing. Image processing
techniques such as texture based, pattern or color based, character recognition etc using different operator or tools such as
Prewitt or Sobel or Canny edge detector, ANN, heuristic analysis, SVM etc are commonly used for recognition and authentication
of paper currency note. Despite several researches it still remains an open challenge. This paper intends to present an extensive
survey of the recent technological trends in recognition and authentication of paper currency note while identifying the various
challenges.
Keywords— Currency recognition, Currency Authentication, feature extraction,Currency Authentication and
Recognition System (CARS)
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1. INTRODUCTION
The expansion of modern banking services necessitates the
needs for automatic currency recognition and authentication
system, thus encouraging many researchers to develop high
accuracy, reliable and high processing speed techniques. To
detect the authenticity of currency note there are two
methods i.e first line inspection method and second line
inspection method. First line inspection method includes
varied density watermarks, ultraviolet fluorescence, intaglio
printing, microtext and hologram while the second line
inspection methods include isocheck/isogram, fiber based
certificates of authenticity, color and feature analysis. First
line inspection method is used for on-spot authentication of
notes but is easier to counterfeit than the second line
inspection method while the second line inspection method
requires an extra device to perform the inspection.
The main challenge for currency recognition is to identify
currency note in a bunch of currency notes where there is a
probability of finding different denomination note. Also a
robust system should be able to recognize worn, blurry,
defaced and even damaged note during circulation by
human being.
2. CURRENCY NOTE AUTHENTICATION
METHOD
The Currency note Authentication method can be broadly
classified into: 1) First Line Inspection Method
2) Second Line Inspection Method
2.1 First Line Inspection Method
The various first line inspection methods are discussed
below:
a) Watermarks: The Mahatma Gandhi series of
banknotes contain Mahatma Gandhi watermark. The
banknote paper has a quality of varying density and is
printed in a controlled manner. When bright light is
illuminated on the rear side of the note the watermark
become visible on the other side of the note due to
varying light intensity due to varying density [1].
b) Ultraviolet fluorescence: The security thread, some
of the fibers embedded on the note between watermark
& denominational value and the serial number of the
note are printed in fluorescence ink which becomes
bright when illuminated with UV led from the rear
side of the note [1].
c) Intaglio printing: In intaglio printing the note is
subjected to high pressure printing process which
raises the paper surface. The portrait of Mahatma
Gandhi, the Reserve bank seal, guarantee and
promise clause, Ashoka pillar emblem, RBI governor
signature are printed in intaglio, which can be felt by
touch in Rs 20, Rs 50, Rs 100, Rs 500 and Rs 1000
currency notes. A special feature in intaglio known as
identification mark has been introduced on left of the
watermark window on all notes except Rs 10. This
feature is in different shapes for different
denomination (Rs 20-Vertical rectangle, Rs 50-Square,
Rs 100-Triangle, Rs 500-Circle, Rs 1000-Diamond). It
helps visually impaired to identify the denomination
by touching [1].

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d) Micro-lettering: Some texts are printed in less than 1
point size between vertical band and Mahatma Gandhi
portrait. It is readable only with magnifying glass. In
Rs 5 and Rs 10 note the word „RBI‟ is present. In the
notes Rs 20 and above it also contain the
denominational value in microletters [1].
e) Security thread: The security thread appears to the
left of the Mahatma Gandhi portrait. The Rs 5, Rs 10,
Rs 20 and Rs 50 notes contains a readable, fully
embedded windowed security thread with inscription
„Bharat‟ (in Hindi) and „RBI‟. The Rs 100, Rs 500 and
Rs 1000 notes contains readable, windowed security
thread alternatively visible on obverse with inscription
„Bharat‟(in Hindi) and „RBI‟ but the Rs 1000 note
contain additional inscription „1000‟ in the note[1].
f) Optically variable ink: A new security feature has
been added to notes of Rs 500 and Rs 1000 in
November 2000. The numeral 500 and 1000 are
printed with optically variable ink viz, a color shifting
ink. The color of the numeral is green when note is
held flat but changes to green when note is held at an
angle.
g) Identification mark: An identification mark has been
introduced in the note for the benefit of the visually
impaired people. This mark is in intaglio printing & it
is in different geometrical shapes for various
denominations as shown in fig 1.1.
h) Latent image: It is a vertical band present on the right
side of Mahatma Gandhi‟s portrait showing the
respective denominational value in numeral when the
note is held horizontal at the eye level.
i) See through Register: The small floral design printed
both on the front (hollow) and back (filled up) of the
note in the middle of the vertical band next to the
Watermark has an accurate back to back registration.
The design will appear as one floral design when seen
against the light.
Generally first line inspection method is used by people for
on spot verification of authenticity of the note being
exchanged. It is an inexpensive method since it doesn‟t uses
any device for verification. But the disadvantages of this
method are that it is easy to counterfeit because it is as
visible to the counterfeiter as to the verifier [1].
Fig 1.1 Identification mark for various denominations [2]
Fig 1.2 Security features in a banknote [2]

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Fig 1.3 New security features added in new M.G series notes[2]
2.2 Second Line Inspection Method
a) Counterfeit detection pen: It is a device that is
designed to determine whether the note is fake or
genuine. The pen contains iodine as ink which is
brown in color. The iodine when reacts with starch,
which is a primary component that makes white paper
looks brighter. The commercial paper is generally
brown in color unless it is bleached and starched. The
ink will turn black if the note is fake but will remain
amber or brown when the note is genuine.
The major limitation of this pen is that when a fake
note made from the paper which is not bleached or
starched, the pen will detect it as a genuine note [3].
b) Digital image processing: The digital image
processing techniques involve extraction of the
features from the test image and then comparing with
pre stored original image. The different steps in
authentication of a note are:
i. Image acquisition: An image is obtained from
the digital camera with white background. The
main challenge in this image processing will
be to process the image with background other
than white.
ii. Image preprocessing: The image obtained
from the digital camera is too big. In order to
reduce the calculation, the size of the image is
reduced with the help of image interpolation.
iii. Edge detection: Edge of gray scale image is
detected by using any one of the operator such
as Sobel, Canny, Prewitt, Roberts and
Laplacian of Gaussian methods. Among these
operator Canny edge detector is more powerful
and can detect true weak edges [4].
iv. Image segmentation: It is a process of
dividing the image into its constituent regions
or objects. Segmentation algorithm for
monochrome images is based on one of the
two basic properties of image intensity values
i.e Discontinuity and similarity. By using
discontinuity property image is partitioned
based on abrupt changes in intensity while by
using similarity property image is partitioned
into regions according to a set of predefined
criteria [5].
v. Feature extraction: The main task of feature
extraction is to find the correspondingly
effective features out of many pending features
such as serial no, watermark, security thread,
identification mark.
vi. Matching: The extracted features are then
compared with original currency features. If
the pixel of the segmented image of test
currency matches with the segmented image of
original currency then the currency is found to
be genuine otherwise counterfeit.
In [6], Rubeena Mirza and Vinti Nanda extracted the general
attributes of paper currency that is shape including
identification mark, security thread and watermark using
edge based segmentation method by Sobel operator. After
extracting the features the background is separated. The
extracted features are compared with extracted features of
original test currency by calculating the number of black
pixels.

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In another image processing based work [1] recognition of
fake note was done by dividing the currency note into some
parts and then each part was processed individually using
SVM (Support Vector Machines). It is mainly used to
determine the location of decision boundaries that produce
optimal separation of classes. They also used character
recognition to check the serial number of notes with the help
of heuristic analysis. Here they have used ANN to train and
test the whole system. For advanced authentication they
have used UV and infrared light system to detect the
watermark, latent image etc.
D. Alekhya, G.Devi Surya Prabha & G. Venkata Durga Rao
has also done fake currency note detection using image
processing. Here they first segmented the red, green and
blue components of the original and the test currency. Then
a new image of combination of different component is
taken. But the image with the combination green component
of test image is taken since the human eye is sensitive to
green. The newly constructed image is compared with the
original currency image. Then threshold value of
equivalence is calculated to test whether the note is fake or
real. When components of two images are combined then if
the note tested is original then only at the place of number
we get variation otherwise if the tested note is fake then
image overlapping will not be proper [3].
In paper [7] and [8] the author used printing technique to
authenticate a currency note because different printing
processes are used to print different parts of a note. All these
printing processes are applied not at a time but in a sequence
which is a security aspect. They have used three different
classifier i.e. Support Vector Machines (SVM), Neural
Network (NN) and Fisher Linear Discrimination Analysis to
authenticate the printing process.
In paper [9] the author proposed an effective system to
detect counterfeit currency notes by processing the image of
currency note taken by IR camera which is illuminated by
IR led. By testing 1750 banknotes have attained 95.7%
efficiency.
c) UV illumination method: The currency note has the
property that it absorbs the UV light and the fake note
reflects the UV light. Therefore this feature can be
used for detection of authenticity of currency note. For
this a UV transmitter and a receiver or detector is used.
The UV light transmitted by the transmitter is
absorbed by the genuine note and is reflected by the
fake note. Depending upon the light received by the
receiver the note is detected as genuine or fake [10].
Another method using UV illumination is a
combination of both UV illumination and image
processing. Here first the note is illuminated by UV
light because some of the patterns are not visible under
normal LED illumination. The image obtained is
converted into gray image to find the gray histogram.
The image is separated into a pattern and a background
by using threshold value. There are two methods for
separation. Otsu‟s and Huang Wang‟s method based
on histogram threshold are used in separating an image
into two objects. Another method is Gaussian mixture
model (GMM) which can segment image into many
objects. After this pattern matching is performed to
detect the authenticity [10].
The second line inspection methods are more secure and
harder to counterfeit than the first line inspection methods
which are only visual based. It also adds an extra cost of
device to perform verification.
3. BASIC STEPS OF A GENERAL IMAGE
PROCESSING BASED CURRENCY NOTE
RECOGNITION METHOD
The word recognition means to identify objects in an image.
The process of recognition starts with image processing
techniques. The different steps in currency recognition are:
i. Image acquisition
It is a process of acquiring image from a currency
note by using digital camera. The image is stored for
further processing.
ii. Image Pre-Processing
Image pre-processing is done to enhance some
image features important for further processing and
analysis. In image pre-processing size of the image
is reduced and noise is removed that may have
appeared in the image while transferring.
iii. Edge detection
It aims at identifying points in digital image at
which image brightness changes sharply. It is an
important step in image processing and analysis.
iv. Binarisation & Background subtraction
During binarisation a given image is converted into
binary image. Binarisation uses the concept of
thresholding where individual pixels in an image is
marked as „object pixels‟ if their value is greater
than the threshold value and as background pixels
otherwise. Thus binarisation process leads to
background subtraction.
v. Feature extraction
Feature extraction involves extraction and selection
of some effective and important features out of
many features which are extremely important in
recognition of notes. Some features of an image are:
 Size or Area
There is a difference in size of different
denomination currency note. Therefore size can
be used as a feature for currency recognition.
But the major limitation of this feature is that
size of the image vary depending on the
distance from which photo of the image has
been taken. To overcome this problem a new
feature has been introduced i.e aspect ratio [11].

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 Aspect Ratio:
It is the ratio of width of the note to that of
height of the note. Unlike the size of the image
the aspect ratio of note of particular
denomination is independent of the distance
from which the photo of the image has been
taken.
 Color
There are varieties of colors present in a
currency note. But there is one or two dominant
color present in each note which can be used for
distinguishing between different currencies
note.
 Euler Number
Euler number is the number of objects in a
image minus the total number of holes in an
image. MATLAB has in built function to find
the Euler number of image [12]
 Texture
There is not much difference in the size and
color of different denomination currency of a
particular country. So these features may not be
able to easily differentiate between different
currencies. The Markov chain concept is used to
model the texture as random process.
 Matching algorithm
It recognizes currency notes of different
denomination based on the various unique
features extracted. In this method comparison of
the values of the features with the predefined set
of features is done. If the values fall within the
specified threshold range, the denomination will
get detected.
4. TYPES OF CURRENCY RECOGNITION
TECHNIQUES
4.1 Texture based Recognition Technique
Texture based currency recognition is a very useful feature.
The texture provides the measure of properties such as
smoothness, coarseness and regularity. To describe the
texture of a region the three principal approaches used in
image processing are statistical, structural and spectral.
Statistical approaches yield characterizations of textures as
smooth, coarse, grainy and so on. Structural techniques deal
with description of texture based on regularly spaced lines.
Spectral techniques are based on properties of Fourier
spectrum and are used primarily to detect global periodicity
in an image [13].Some texture features that have been used
for image retrieval are Tamura features, MRSAR, edge
histogram, Gabor texture feature, and pyramid-structured
and tree-structured wavelet transform. Tamura features
define the coarseness, directionality and contrast of an
image. Tamura coarseness is the measure of average of
coarseness at each pixel location inside the texture region.
The performance of this feature is not good since it compute
directly from image. So the performance can be improved
by using histogram. MRSAR (Multi Resolution
Simultaneous Auto Regressive) gives results which is
helpful in distinguishing different texture patterns. Gabor
feature extract information at multiples scales and
orientation. According to author MRSAR and Gabor feature
give better result than other feature. Visual texture doesn‟t
have any strict definition and it is generally the presence of
repetitive patterns in which elements or primitives are
arranged according to some placement rule [13].
4.2 Pattern based Recognition Techniques
Pattern based recognition is basically classification of
objects based on some set of images. It requires
experimenter to have some prior knowledge about the
objects to come to a conclusion. It also requires a large
collection of data. There are many techniques that make use
of pattern recognition. All these techniques uses vector
quantization based histogram modeling. Vector quantization
is a sampling method where each point xj in a set of data in
d-dimensional space is replaced by L prototype points. The
prototype points are picked in such a way that sum of the
distances (called the distortion) from each data points xj to
its nearest prototype point is minimized.
Some work has been done in this area by Seth Mc Neillin
for recognition of coin by pattern recognition. In this
process first he collects the data by collecting coins with
different background. Next the segmentation and cropping is
done. In this step coins are segmented from background by
using modified Nechba‟s code. Cropping of the image is
done by locating the edges of the coin. Then features were
extracted by convolving texture templates with each image.
Next step is the training the program with as many data as
possible to get high accuracy. Here the author uses five set
and get 94% accuracy [13].
The work in this area is done by Vipin kumar Jain for Indian
currency note. In this process he uses digital image
processing techniques to find the region of interest and after
that Neural Network and pattern recognition techniques is
used for matching the pattern. From the experiment he
found that recognition method of Indian currency note is
quite simple, efficient and easy to be realized because
denomination numerals are used for identification which can
be extracted easily from paper currency. Then extracted
numerals are matched and are found exact match for
identification. By using this recognition technique counting
of different denomination note in a bunch can be done [14].
Dipti Pawade et al.(2013) made a comparative study on
various currency recognition technique. In one of the paper
by Kalyan Kumar Debnath et al proposed a method for
currency recognition using ensemble neural network(ENN)
particularly for Bangladesh currency. Ensemble neural
network consist of number of neural network and each
neural network is trained independently. Here each neural
network is trained via negative correlation learning. The
system is able to recognize highly noisy or old image and
reduces the misclassification of notes [15].

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Faiz M. Hasanuzzaman et al. proposed a component based
framework for banknote recognition by using Speeded Up
Robust Features (SURF). SURF feature divides the image
into components and then matches test image with original
image component. The SURF features are invariant to
conditions of image occlusion, image rotation, changes of
scaling, illumination and the viewpoint. The proposed
algorithm achieves 100% accuracy [16].
In paper [17] the author proposed a method of currency
recognition using Neural Network. Here they have used
color, size and texture as three features that have used to
recognize the currency note. In the experimentation they
have achieved 97.34% accuracy.
4.3 Colour based Recognition Techniques
Variety of colors is used in each paper currency but there is
only one or two dominant color present in each paper
currency which can be used for distinguishing each paper
currency. The image delivered from the digital camera is in
RGB format. The electronic processing of these color
signals (Red, Green and Blue) can take a number of
different representation formats denoted as color spaces.
There are variety of color spaces YIQ, HSV, YUV, HIS and
YCrCb and each format has its own advantage. The choice
of color space has a great influence on the performance of
procedures such as segmentation. After taking the image
from the digital camera the dimensionality of data is
reduced. Then the image is converted from the RGB color
space to a color space suitable for processing. After
processing the features are extracted, matched and currency
is detected as genuine or counterfeit.
In 2012 Pragati D. Pawar and Shrikant B. Kale converted
image from RGB color space to HSV color space before
feature extraction. They used HSV color space because it is
close to human conceptual understanding of color. The
feature extraction is done by analyzing color histogram, hue,
saturation and intensity value. Then color histogram, hue,
saturation and intensity value of input image is compared
with the saved images. If the difference in threshold value is
greater than the specified value then the currency is genuine
otherwise counterfeit [18].
Histogram describes the global color distribution in an
image by counting the number of pixels of each color. Color
histogram method will suit when we have to segregate
between the range of color and prominent color. The major
limitation of color histogram is that color histogram
describes which colors are present in the image and in what
quantity but doesn‟t provide spatial information [10].
John R Smith and Shih Fu Chang has also done work in this
area. They propose the techniques for color image retrieval.
Here color indexing techniques is used by which images are
extracted on the basis of color content of the image. The
color indexing algorithm uses back-projection of binary
color sets to extract color region from the image. It
overcomes some of the problem with color histogram [19]
The major limitation of color based recognition techniques
is to recognize an old note whose color become fade or
changed due to rigorous circulation.
The three currency recognition techniques discussed above
requires a user to always carry a machine along with
himher which consumes a lot of power. To overcome the
problem a new technique has been developed known as
currency localization technique.
4.4 Currency Localization Techniques
Currency localization technique is a mixed approach where
first currency note in an image is localized and then various
threshold based algorithm are applied to determine the
denomination of currency note.
In this technique first image is taken by a mobile camera or
a webcam. Then some enhancement of the image is required
by applying some pre-processing technique such as noise
reduction, normalization and contrast enhancement. Next
background subtraction, RGB to gray conversion and edge
detection is performed. Localization of currency note is
done by applying scan line algorithm on the image after
edge detection. The image is scanned from left to right and
top to bottom line by line. The line is highlighted which
contain number of pixels greater than the set threshold. With
the intersection of these lines a rectangle is formed which
surrounds the currency note. Then color matching technique
is used for currency recognition.
Hanish and Padam has done currency recognition in color
images by applying currency localization technique. In this
experiment they got 96% efficiency. The system can be
enhanced by using template matching [13].
Parminder Sigh Reel, Gopal Krisnan and Smarti Kotwal has
done some work on currency note recognition based on
heuristic analysis of character and digits of serial number.
Heuristic analysis is needed to filter non- character elements
which are not reliably separated by traditional OCR method.
Heuristic analysis of color separates character and non-
character elements based on color basis [20].
Suriya Singh et al presented an application for Indian
currency recognition on mobile. For this they have used
visual Bag of Words (BoW) recognition method. Here they
have used GrabCut algorithm to segment the foreground
from background. This process of segmentation work so
well that it can segment image from the cluttered
background. After testing 2584 images they have got 96.7%
accuracy [21].
In [22], the authors proposed a method for currency note
recognition by using ANN. Here a special linear
transformation function is adapted to wipe out the noise
pattern from background without affecting the note original
feature. The edge detection feature along with linear
transformation is a better feature extraction technique and
helps to keep notes look similar in varying conditions.

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5. CONCLUSION
Detecting the authenticity and recognition of currency note
has become important nowadays because of the prevailing
fraudulent activities as it hampers our economy. It is very
useful in the banking system as well as in other commercial
applications.
There are many methods used to detect authenticity and to
recognize a note which is being discussed in this paper and
each one has its own objective and significance. In this
paper we have discussed many tools that can be used to
recognize different features of a note. Since a thing can‟t be
perfect and has certain limitation. So selection of feature and
the selection of tools to recognize that particular feature are
critical. To overcome this limitation and building an
interactive system with high speed, accuracy and cheap is a
new challenge.
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