- 1. Presented By Vijayalakshmi.S.L Under the Guidance of
Mrs.Smita Gour Department of Computer Science & Engineering
Basaveshwar Engineering College Bagalkot
2. Contents Introduction Literature Survey Problem Definition
Proposed Methodology Experimentations Conclusion and future work
References 3. Introduction Nowadays, person identification
(recognition) and verification is very important in security and
resource access control. Biometrics is the science of automatic
recognition of individual depending on their physiological and
behavioral attributes. For centuries, handwritten signatures have
been an integral part of validating business transaction contracts
and agreements. Among the different forms of biometric recognition
systems such as fingerprint, iris, face, voice, palm etc.,
signature will be most widely used. 4. Signature Recognition
Signature Recognition is the procedure of determining to whom a
particular signature belongs to. Depending on acquiring of
signature images, there are two types of signature recognition
systems: Online Signature Recognition Offline Signature Recognition
5. Literature Survey 1. Offline Handwritten Signature
Recognition(Gulzar A. Khuwaja and Mohammad S. Laghari) Biometrics,
which refers to identifying an individual based on his or her
physiological or behavioral characteristics, has the capability to
reliably distinguish between an authorized person and an imposter.
This paper presents a neural network based recognition of offline
handwritten signatures system that is trained with low- resolution
scanned signature images. 6. 2. Off-line Signature Verification
Based on Fusion of Grid and Global Features Using Neural
Networks(Shashi Kumar D R and K B Raja) In this paper Off-line
Signature Verification Based on Fusion of Grid and Global Features
using Neural Network(SVFGNN) is presented. The global and grid
features are fused to generate set of features for the verification
of signature. 7. 3. DWT based Off-line Signature Verification using
Angular Features (Prashanth C R ) This papers presents DWT based
Off-line Signature Verification using Angular Features (DOSVAF).
The signature is resized and Discrete Wavelet Transform (DWT) is
applied on the blocks to extract the features. 8. 4. Off-Line
Signature Recognition Systems(V A Bharadi) Handwritten signature is
one of the most widely used biometric traits for authentication of
person as well as document. In this paper we discuss issues
regarding off-line signature recognitions. The performance metrics
of typical systems are compared along with their feature extraction
mechanisms. 9. 5. Offline Signature Recognition and Verification
Based on Artificial Neural Network(Mohammed A. Abdala) In this
paper, a problem for Offline Signature Recognition and Verification
is presented. A system is designed based on two neural networks
classifier and two powerful features (global and grid features).
The designed system consist of three stages which is pre-
processing, feature extraction and neural network stage. 10. 6.
Signature Recognition & Verification System Using Back
Propagation Neural Network (Nilesh Y. Choudhary, Dr. Umesh.
Bhadade) In this paper, off-line signature recognition &
verification using back propagation neural network is proposed
which is based on steps of image processing, invariant central
moment & some global properties and back propagation neural
networks. 11. Problem Definition Signature Recognition is the
procedure of determining to whom a particular signature belongs to.
In this work, the global and grid features are combined and used to
differentiate among the signature images. These combined features
are given to Back Propagation Neural Network(BPNN) to train it, so
that particular signature image is recognized. 12. Proposed Model
Block Diagram of Signature Recognition 13. Image Acquisition :
Collection of signatures from 50 persons on blank paper. The
collected signatures are scanned to get images in JPG format to
create database. 14. Pre-Processing : Image pre-processing is a
technique to enhance raw images received from cameras/sensors
placed on satellites, space probes and aircrafts or pictures taken
in normal day-to-day life for various applications. The techniques
for preprocessing used are RGB to Gray Scale Conversion
Binarization Thinning Bounding Box 15. RGB to Gray-Scale Convertion
Binarization RGB Image Gray-Scale Image Gray-Scale Image Binarized
Image 16. Thinning Bounding Box Binarized Image Thinned Image
Thinned Image Bounded Image 17. Feature Extraction Features are the
characters to be extracted from the processed image. It has used
two feature techniques Global Features Grid Features 18. Global
Features Height : Width : Number of Black Pixels : Centroid of the
signature : Width Height 19. Grid Features The cropped image is
divided into 9 rectangular segments i.e. (3 3) blocks. 3*3 Blocks
of Grid Image 20. DWT(Discrete Wavelet Transform) DWT applied on
1st block. Each block contributes horizontal, vertical and diagonal
components. 1st Block Horizontal Vertical Diagonal 21. After
applying DWT to all 9 blocks, each block is divided into
horizontal, vertical and diagonal components. From each components
two features mainly horizontal and vertical projection positions
are extracted. Total 54 (9 x 3 x 2) features are extracted. Grid
features extracted from each block are Horizontal Projection
Position: Vertical Projection Position: 22. Total 54 features
extracted by 9 blocks 23. Classification What is Neural Network..?
Why Neural Network..? What is Back Propagation Neural
Network(BPNN)? 24. BPNN Architecture Architecture of Back
Propagation Neural Network 25. Training of BPNN This involves
developing a suitable neural network model (BPNN). Then the
extracted features are presented to BPNN, which recognizes the
different types of signature images. The training takes place such
that the neural network learns that each entry in the input file
has a corresponding entry in the output file. 26. Run Snapshot of
BPNN 27. Algorithm for Training phase Description: Retrieval of a
signature image from a database Input: Training sample images.
Output: Construction of Back Propagation Neural Network. Begin Read
the training samples images Step1: Pre-processing Convert the image
into gray scale image. Convert the gray scale image into binary
image. Apply thinning process. Apply bounding box. 28. Step 2:
Features Extracted. Step 3: Back propagation neural network
training. end // end of proposed algorithm 29. Testing using
Trained BPNN In testing, input image from testing set is selected
and its features are extracted and given them to the trained model,
the trained BPNN model classifies given sample and produces output
as type of signature and corresponding pattern Classification
accuracy= Number of recognized signatures Total number of testing
signatures 30. Output Pattern for Recognition 31. Experimental
Results Experiment 1 The features extracted are listed as: Height
of the signature Width of the signature Centroid of X-axis and
Y-axis Number of black pixels of the signature The image is divided
into 9 blocks and DWT is applied to each block. Energy values of
each block were extracted as a feature. 32. 10 20 30 40 50 60 70 80
90 100 10 20 30 40 50 83.46 81 78.28 76.7 74 Performance Rate
Performance Rate No of Persons Performance Rate of 1st Experiment
33. Experiment 2 The features are extracted as listed below: Height
of the signature Width of the signature Centroid of X-axis and
Y-axis Number of black pixels of the signature The image is divided
into 9 blocks and DWT is applied to each block. From each block two
features, horizontal and vertical projection positions of
horizontal, vertical and diagonal components are extracted 34. 10
20 30 40 50 60 70 80 90 100 10 20 30 40 50 93.33 92.91 91.38 90
89.47 Performance Rate Performance Rate No of Persons Performance
Rate of 2nd Experiment 35. No. of Persons Experiment 1 Experiment 2
10 83.46 % 93.33 % 20 81 % 92.91 % 30 78.28 % 91.38 % 40 76.7 % 90
% 50 74 % 89.47 % Performance Rate The performance rate of the two
experiments 36. Conclusion The objective of signature recognition
is to recognize the signer for the purpose of recognition. It has
been observed that the global and grid features extracted using
discrete wavelet transform are found to be efficient for offline
signature recognition. The combination of discrete wavelet
transform and back propagation neural network has given expected
results. It achieved the accuracy rate ranging from 93%-89% for
enrollment of 10 to 50 persons. 37. Future Work The signature
recognition can also be changed by changing the features that can
be extracted from a signature. So, the future work of the
recognition of signature can be done with the same Neural Network
methods but using different signature features and compares the
results with results of the present project. 38. References Gulzar
A. Khuwaja and Mohammad S. Laghari, World Academy of Science,
Engineering and Technology , Offline Handwritten Signature
Recognition, 2011 Shashi Kumar D R, K B Raja, R. K Chhotaray,
Sabyasachi Pattanaik, Off-line Signature Verification Based on
Fusion of Grid and Global Features Using Neural Networks, 2010
Prashanth C R , K B Raja, Venugopal K R, L M Patnaik, DWT based
Off-line Signature Verification using Angular Features, 2012 V A
Bharadi, H B Kekre, Off-Line Signature Recognition Systems, 2010
Mohammed A. Abdala & Noor Ayad Yousif, Offline Signature
Recognition and Verification Based on Artificial Neural Network,
2008 H. Baltzakis, N. Papamarkos, A New Signature Verification
Technique Based On A Two-Stage Neural Network Classifier, 2001
Khamael Abbas Al-Dulaimi, Handwritten Signature Verification
Technique based on Extract Features, 2011 39. Hemanta Saikia, Kanak
Chandra Sarma, Approaches and Issues in Offline Signature
Verification System, 2012 Vu Nguyen, Michael Blumenstein, Graham
Leedham, Global Features for the Off-Line Signature Verification
Problem, 2009 Meenakshi S Arya, Vandana S Inamdar, A Preliminary
Study on Various Off-line Hand Written Signature Verification
Approaches, 2010 Javed Ahmed Mahar, Prof. Dr. Mumtaz Hussain Mahar,
Muhammad Khalid Khan, Comparative Study of Feature Extraction
Methods with K-NN for Off- Line Signature Verification, 2006 Nilesh
Y. Choudhary, Mrs. Rupal Patil, Dr. Umesh. Bhadade, Prof. Bhupendra
M Chaudhari,Signature Recognition & Verification System Using
Back Propagation Neural Network, 2013 Manoj Kumar, Signature
Verification Using Neural Network, 2012 Paigwar Shikha and Shukla
Shailja,Neural Network Based Offline Signature Recognition and
Verification System, 2013 Srikanta Pal, Michael Blumenstein,
Umapada Pal, Off-Line Signature Verification Systems: A Survey,
2011 40. Thank You