download abstract

If you can't read please download the document

  • date post

  • Category


  • view

  • download


Embed Size (px)

Transcript of abstract

Chapter 1.


1.1Abstract 1.2Project overview 1.3Introduction and Motivation1.4 Problem Definition

1.5Literature Review Technical Paper 1.6Scope Chapter 2. Project Design: Development Model 2.1Lifecycle Model 2.2 Requirement Analysis : Feasibility and Risk Analysis 2.3Software Requirements Specification Document 2.4Software Design Document 2.5UML Diagrams / DFD , CFD, ERD Chapter 3. Project Management Plan

3.1Software Architecture 3.2Task & Responsibility Assignment Matrix 3.3Project Timeline Chart Chapter 4. Project Implementation (Implementation Details) 4.1Approach / Main Algorithm / Methodology 4.2Programming Language used for Implementation 4.3Tools used Chapter 5. Integration & Testing

5.1Testing Approach 5.2Testing Plan 5.3Unit Test Cases 5.4Integrated System Test CasesChapter 6.

Conclusion&Future work (Enhancements) References

Chapter 7.

Appendix I) Minimum System Requirement II) Users Manual III) Technical Reference Manual IV) Data Sheets of chips used ( for hardware projects only)

1. Introduction1.1) ABSTRACT Signature is the characteristic of the particular person and hence used globally for identifying a person, validity of the documents signed, banking etc. Up till now, in banks where signature of a person is the basic code for transaction, the validity of the signature is generally checked by a man. Our project simulates the ability of a man to

recognize a signature from the set of samples of signatures. A signature verification system may be either offline or online. The signature is captured using an optical scanner and stored in an image format (GIF).Then the image is converted into a bit pattern from which features are extracted. These features are said to be static. Artificial Neural Network is used in our project. Our system is trained to identify similarities and patterns among different signature samples. Any given signature is verified based on training that is provided. Our project incorporates database management, image preprocessing, feature extraction, learning and verification modules (Artificial Neural Networks).

1.2) PROJECT OVERVIEW Our project is designed to speed up the process of verification and minimize manual intervention. Our system automates the process of recognition and verification of signatures. The features of standard signatures of customers along with their account number and their name are stored in the database. The Artificial Neural Network is trained using the features extracted from the database. The operator will provide the customers signature in the form of an image. The features extracted from this signature image are used to test the system (Recognition and Verification). The output of neural network provides a serial number which corresponds to a customer record in the database. The error rate is calculated by comparing the features of original signature image with the features of signature image to be tested. This error rate should be less than the threshold value for the signature to be recognized and genuine. STEPS IN SIGNATURE RECOGNITION A) Image Pre-processing: The data is transformed to a standard format. 1. Conversion of colored to gray scale image. 2. Image Enhancement 3. Noise Reduction 4. Image Normalization 5. Image Thinning 6. Cropping B) Feature extraction: converts each image into a set of binary features. 1. Global feature 2. Moment Invariant Method

C) Artificial Neural networks: It is used for identification and verification. Back Propagation neural network is used. 1.3) INTRODUCTION AND MOTIVATION There exist a number of biometrics methods today e.g. Signatures, Fingerprints, Iris etc. There is considerable interest in authentication based on handwritten signature verification system as it is the cheapest way to authenticate the person. Signature verification does not require the installation of costly equipments and hence can be used at day to day places like Banks etc. The objective of the project is to make software for Offline Signature Recognition and Verification. It involves recognition of signature that has been read optically. The method starts with a scanned image of a handwritten signature. A signature is treated as an image carrying a certain pattern of pixels that pertains to a specific individual. There is a growing interest in the area of signature recognition and verification (SRVS) since it is one of the important ways to identify a person. Recognition is finding the identification of the signature owner [1]. Verification is the decision about whether the signature is genuine or forged. Signature is a special case of handwriting in which special characters and flourishes are viable. Signature verification is a different pattern recognition problem as no two genuine signatures of a person are precisely the same. Signature Recognition and Verification (SRVS) are categorized into two major classes: On-line Signature Recognition and Verification System Off-line Signature Recognition and Verification System

The difference between the off-line and on-line lies in how data are obtained. In the on-line SRVS data are obtained using special peripheral device, while in the off-line SRVS images on the signature written on a paper are obtained using scanner or a camera.

1.4) PROBLEM DEFINITION Our project is designed to speed up the process of verification and minimize manual intervention. Our system automates the process of recognition and verification of signatures. The operator who is using Offline Signature Recognition and Verification software will provide the customers signature in the form of an image. Our system is able to recognize the owner of the image and provides the identification number and name of the customer to whom the signature belongs to. Our system recognizes all signatures that the artificial neural network is trained for.


1] Integration of Offline and Online Signature Verification System By: Deepthi Uppalapati, Department of Computer Science & Engineering, Indian Institute of Technology, Kanpur, India, July 2007 In this thesis, an integrated verification system has been proposed in which the feature vector comprises of static and dynamic features. It not only provides a way to match and compare an online signature versus an offline signature and vice versa, but also improves the system performance. 2] Signature recognition and Verification with ANN By: Cemil oz, Fikret Ercal, Zafer Demir, 2005 In this paper, we present an off-line signature recognition and verification system which is based on moment invariant method and ANN. Two separate neural networks are designed; one for signature recognition, and another for verification (i.e. for detecting forgery). Both networks use a four-step process. First step is to separate the signature from its background. Second step performs normalization and digitization of the original signature. Moment invariant vectors are obtained in the third step. And the last step implements signature recognition and verification. 3] Paper on Offline Signature Recognition and Verification Based on Artificial Neural Network By: Mohammed A. Abdala , Noor ayad Yousif, December 2008 In this paper, a problem for Offline Signature Recognition and Verification is presented. A system is designed based on two neural networks classifier and three powerful features (global, texture and grid features). Our designed system consist of three stages: the first is preprocessing stage, second is feature extraction stage and the last is neural network (classifiers) stage which consists of two classifiers, the first classifier consists of three Back Propagation Neural Network and the second classifier consists of two Radial Basis Function Neural Network. The final output

is taken from the second classifier which decides to whom the signature belongs and if it is genuine or forged. The system is found to be effective with a recognition rate of (%95.955) if two back propagation of the first classifier recognize the signature and (%99.31) if all three back propagation recognize the signature. 1.6) SCOPE Our system involves two separate but strongly related tasks: 1) Identification of the signature owner (Recognition) 2) The decision about whether the signature is genuine or forged (Verification) Many of the applications use Offline Signature Recognition as they are simple and do not require any additional tools (like stylus). The features of the standard signatures of the customers along with their name and identification number are stored in the database with the help of tools like Microsoft Access. The signature to be recognized and verified is in an image format. Various image processing and neural network techniques are to be used. The signature image to be tested is identified and verified by using a threshold value.

2) PROJECT DESIGN: DEVELOPMENT MODEL2.1) Lifecycle Model Waterfall model Waterfall Model is used to implement our system. The modules in our project are organized in a linear order. It begins with feasibility analysis and on the successful demonstration of the feasibility analysis, the requirements analysis and project planning begins. The design starts after the requirements analysis is done. And coding begins after the design is done. Once the coding is completed, it is integrated and testing is done. On succeeful completion of testing, the system is installed. After this the regular operation and maintenance of the system takes place. The following figure demonstrates the steps involved in waterfall life cycle model.

fig 2.1: The Waterfall Software Life Cycle Model With the waterfall model, the activities performed in our project are requirements analysis, project planning, system design, detailed design, coding and unit testing, system integration and testing. When the activitie