IJEID :: International Journal of Excellence Innovation and Development (www.ijeid.com)

1. International Journal of Excellence Innovation and Development
||Volume 1, Issue 1, Nov. 2018||Page No. 046-049||
www.ijeid.com {IJEID © 2018} All Rights Reserved Page | 46
Artificial Neural Network Based Offline
Signature Recognition System Using Local
Texture Features
Shivashankar M. Rampur
Professor, Department of Computer Science and Engg., Brahmdevdada Mane Institute of Technology, Solapur Belat Tal.North
Solapur, Distt. Solapur, Maharashtra, India
Abstract––Signature of an individual is a significant
biometric attribute of a human being which can be used
to certify human identity and these attributes can have
own identity like face recognition, fingerprint detection,
iris inspection and retina scanning. In this work, we are
developing method which deals with the off-line
signature recognition system using artificial neural
network in which the signature is captured and presented
in the form of an image to the system. Offline signature
recognition system is a significant biometric technique,
which is used to offers automated process of recognition
and verification by extracting local features that
classifies each input signature and has many number of
uses. The proposed system has local texture features and
feed forward back propagation in an artificial neural
network classifier to identify and authenticate signatures
of individuals. Various image processing techniques are
used to categorize and validate the signature.
Index Terms––Image acquisition, RGB-to-Grayscale
conversion, feature extraction, artificial neural network
INTRODUCTION
Signature is generally acknowledged & used as a way
for authorization in our daily life, which is an important
biometric attributes of human used to verify human
identification. Manual signature is fundamental
procedure for individual, which is used for uncovering of
the document of signer with the assumption that the
signature varies slowly & virtually unfeasible to falsify
without detection. A signature is difficult to replicate and
broadly used to identify an individual delivering his day
by day events such as document study, bank activities,
electronic funds transfer and access control. A signature
as a behavioral biometric encrypts the ballistic actions of
an individual and allows higher intra-class and time
inconsistency, estimates the physical qualities which are
fingerprint, iris or face.
Depending on exhaustion, psychological and physical
state, and lettering location (ergonomics), signatures
vary. The marker accelerations, which are comparative
to the muscle forces exerted by the signer, are reliable in
a usual signature.
Motivation
Offline signature recognition system is a significant
biometric technique, which is used to offers automated
process of recognition and authentication by extracting
local features that classifies each input signature based on
artificial neural network and has many number of uses. The
neural networks is the most outstanding way of finding
solution of the problems that are most difficult to solve by
traditional computational methods. The advantage of neural
network is no need to understand the solution.
While signer is signing, there are variations in terms of pen
width, additions found in strokes, exchange or qualified
point of strokes, scaling within the genuine signatures and
rotation. Our system is motivated to overcome these
variations. Our system gives high level of accuracy.
Objectives
 The objective of our system is used to develop
preprocessing phase which is processed on input
signature image. This preprocessing phase include
conversion of original image into grey scale,
conversion of grey into binary image, noise
reduction, thinning and resize.
 The objective of our system is used to develop
feature extraction phase for classifying signature. In
this phase, we are extracting texture features from
signature which are entropy, homogeneity, contrast,
correlation and energy.
 The objective of our systems is used to recognition
of signature by signers. In this phase, we have to
compare the texture features of test images with
features of train images. If it’s matched then the
given signature is identified else not.
OVERVIEW OF SYSTEM
Offline signature recognition system is an automated
process of detection by extracting local features that
classify each input signature. In this system, we initiate
by images are scanned using scanner, elaborated the
input signature by preprocessing, the extraction of
texture features from the preprocessed images and
analyze the signature with the signature stored in the
knowledge base using classification technique. If its
match then input signature is recognized else not.
Fig. 1 System overview.

2. Artificial neural network based offline signature recognition system Rampur S.M.
www.ijeid.com {IJEID © 2018} All Rights Reserved Page | 47
PROPOSED METHODOLOGY
The proposed methodology consists of several essential
steps which are supposed to be followed with précised
rules. The block diagram of our proposed system gives
the short idea about the procedure of methodology.
Fig. 2: Block diagram of proposed work.
The major steps of the signature recognition are
explained as follows:
Image Acquisition
The action of retrieving an image from the hardware based
source is known as image acquisition, so that it can pass
through the processes which occur after image acquisition.
The first step in the workflow sequence is image
acquisition, because without an image, processing is
impossible. Handwritten signatures are signed on the white
paper by the individuals at different timing under different
emotions such as stress and joy levels and these signatures
are scanned by using scanner of desired dpi resolution in
jpeg image format for the creation of the database.
Pre-Processing
It is a technique to augment raw images by removing
distortions and is the first part of the proposed system
prior to computational processing. We have used RGB-
to-grayscale conversion, gray scale to binary image
conversion, noise reduction, thinning and resizing for
pre-processing.
RGB to Gray-Scale Conversion
In this phase, if the original image is in the form of red,
green and blue which is used to convert to gray scale by
using following formula:
Gray-scale = (0.299*R) + (0.5876*G) + (0.114*B)
It is a general performance in the image preprocessing
phase, since processing of a three channel signature
(colored image) is slower than that of processing a single
channel signature (gray-scale image). This conversion is
shown in Figure 3.
Noise Reduction
A signature images are corrupted due to addition of
unwanted elements or noisy channels and also gets
ruined due to destructive effects caused from lighting
and unwanted elements. To overcome the corruption
caused due to noise, we use median filter for smoothing
and recovering images. Median filter is non-linear
operation which is used to reduce noise and preserve
edges. This conversion is shown in Figure 4.
Thinning
It is one of the morphological procedures, which is
constructed with procedures on pixel sets.
Morphological operations take two arguments: binary
image and structuring element. To conserve the aspect
ratio of signature image, thinned signature image goes
through normalization step. The thinned image consists
of 0’s and 1’s constituted by pixel sets, where the pixels
in the signature become less. This procedure is shown in
Figure 5.
Fig. 3: RGB form of image to gray-scale conversion. (a)
RGB form of image. (b) Gray-scale form of image.
Fig. 4: Noise reduction of image. (a) image contains
noise. (b) noise reduction.
Fig. 5: Thinning of image. (a) binary form of image. (b)
thinned image.
The Feature Extraction Phase
It is the most significant phase in any recognition system
because accuracy of the recognition completely depends
on the features which are going to be extracted from the
signatures. The main purpose of the feature extraction
method is used to get back the features accurately. Here,
we have to extract local texture features like entropy,
correlation, contrast, homogeneity and energy.
Fig. 6: Horizontal division.
(a) (b)
(a) (b)
(a) (b)

3. International Journal of Excellence Innovation and Development
||Volume 1, Issue 1, Nov. 2018||Page No. 046-049||
www.ijeid.com {IJEID © 2018} All Rights Reserved Page | 48
Fig. 7: Block Image divisions.
Classification
This is the step where we obtain approximate or
appropriate result. Classification will be executed on the
basis of features extracted in the feature space.
Classification divides the feature into different classes
based on decision rules. Signature stored in the
Knowledge Base is subjected to classification technique.
We have used Artificial Neural network as classifier in
our signature recognition system. In classification we
compare the test images with trained data images in
order to classify test images.
Fig. 8: Artificial neural network.
The computational model such as artificial neural
networks is stimulated by the brain, which are capable of
recognizing a pattern and machine learning. The
artificial neural networks are generally organized as
interrelated "neurons" and by passing information
through the network the values from inputs can be
determined. In general, neural networks are organized
into three layers such as input, hidden and output layer.
An 'activation function' is held by interconnected ‘nodes’
which are included in neural network. By means of the
'input layer', patterns are given to the network, which
correspond to one or more 'hidden layers’, by using a
system of subjective ‘connections’ the actual processing
is done and then the hidden layers connect to an 'output
layer' which gives output of the system shown in the
figure 8 above.
Many artificial neurons together compose an artificial
neural network and are correlated according to accurate
network design. The goal of the neural network is to
convert the inputs into significant outputs. Here, we have
used an algorithm called feed forward back propagation
in ANN for identification and confirmation of signatures
of individuals, where the simulated neurons are
structured into layers and transmit their signals
“forward” and the errors are transmitted “backward”.
The network collects neurons from the input level and
the output of the network is displayed on an output layer.
RESULTS AND DISCUSSION
The experiment has been approved out so as to estimate
calculated system’s performance. The experiment has
been worked on the database which consists of classes of
95 persons and 10 signatures per individual class where
80 textual features were extracted from each block of the
signature image. The textual features are listed as:
 Energy
 Correlation
 Entropy
 Homogeneity
 Contrast
Identification rate of this experiment is 85-90% which
satisfied our requirement. The performance was checked
against number of person signature also. Initially we
started with database having 20 persons. Gradually we
increased our database by 20 persons in each step. The
following table 1 shows that 20 persons with accuracy.
Table 1: Signatures with accuracy.
Persons signature Accuracy
20 99%
40 98.3%
60 97%
80 96%
95 95%
We have checked performance of 20 signatures of 20
persons; keep adding by 20 persons in each step. We
have trained 20 signatures per each person using feed
forward back propagation algorithm. The performance
rate has checked against signature as shown in following
charts:
Fig. 9: Performance rate.
The above graph shows as a number of individual
increases, performance getting decreases.
CONCLUSION
Offline signature recognition system is a significant
biometric technique, which is used to offers automated
0
20
40
60
80
100
Performance Rate
Performance
Rate

4. Artificial neural network based offline signature recognition system Rampur S.M.
www.ijeid.com {IJEID © 2018} All Rights Reserved Page | 49
process of recognition and authentication by extracting
local features that classifies each input signature and has
many number of uses. The proposed system has local
texture features and feed forward back propagation in an
artificial neural network classifier to identify signature.
By using training algorithm called feed forward back
propagation, we can train a neural network using
extracted features from the signatures. The artificial
neural network has given expected results. Our proposed
system exhibited 85-90%success rate by identifying the
signatures correctly that it was taught for purpose.
Few more efficient features can be included to improve
our system. The performance of the system can be
increased by using additional features in dataset. We can
increase accuracy of identification by increasing
signature of trained images for each individual. Our
system gives 90-99% accuracy based on number of input
signature.
REFERENCES
[1] J.P. Drouhard, R. Sabourin, M. Godbout,
“Evaluation of a Training Method and of Various
Rejection Criteria for a Neural Network Classifier
Used for Off-Line Signature Verification”, IEEE
Int’l Conf. Neural Networks, Orlando, Fla., June
26-July 2, pp. 294-4,299, 1994.
[2] R. Baeza-Yates, G. Valiente, “An Image
Similarity Measure Based on Graph Matching”,
IEEE University of Chile, 2000.
[3] E. J. R. Justino, F. Bortolozzi, and R. Sabourin,
“Off-Line Signature Verification using HMM for
Random Simple and Skilled Forgeries”, Proc. 6th
Intl. Conf. On Document Analysis and
Recognition, 2001, pp. 450-453.
[4] Xiao, X. and Leedham, G (2002), “Signature
Verification using a Modified Bayesian Network.
Pattern Recognition”, 2002, vol. 35, no. 5, pp.
983-995.
[5] B. Fang, C.H. Leung, Y. Y. Tang, K. W. Tse, P.
C. K. Kwok and Y. K. Wong, “Off-Line Signature
Verification by the Tracking of Feature and Stroke
Positions,” Pattern Recognition, vol. 36, pp. 91–
101, 2003.
[6] Vamsi Krishna Madasu, “An Automatic Offline
Signature Verification and Forgery Detection
System”, University of Canberra, Pattern
Recognition Technologies and Applications:
Recent Advances, 63-89, 2004.
[7] M. Hanmandlu, M. H. M. Yusof, and V. K.
Madasu, "Off-Line Signature Verification and
Forgery Detection using Fuzzy Modelling”,
March-2005, vol. 38, pp. 341-356.
[8] Johannes Coetzer, “Off-Line Signature
Verification”, Journal, University of Stellenbosch,
April -2005, 45-90.
[9] Juan J. Igarza , Inmaculada Hernáez, Iñaki
Goirizelaia, Koldo Espinosa and Jon Escolar,
“Off-Line Signature Recognition based on
Dynamic Methods”, Dept. of Electronics and
Telecommunications, University of the Basque
Country Alameda Urquijo s/n, Bilbao, Spain
E48013, vol.5779,2005.
[10] Dakshina Ranjan Kisku, Phalguni Gupta and
Jamuna Kanta Sing, “Fusion of Multiple Matchers
using SVM for Offline Signature Identification”,
Communications in Computer and Information
Science, 2009, Volume 58, pp. 201-208.
Biography of Author
Mr. Shivashankar M. Rampur
received the B.E and M.Tech degrees in Computer
Science and Engineering from Basaveshwar Engineering
College, Bagalkot, Karnataka in 2012 and 2016,
respectively. Presently working as Assistant professor at
Brahmdevdada Mane Institute of Technology, Solapur.