1. Signature Verification
Presented By:
Arpit Jain
04226g-CSE
Under the guidance of
Dr.Vipin Tyagi

2. Content: About Pattern Recognition and its application
About Signature Verification.
Difference from Character Recognition
Introduction of Signature Verification
Categories of Signature Verification
Approaches
Neural Network
Application
Tools used
References

3. About:
Basically, Signature verification is subtopic of Character Recognition and Character Recognition is subtopic of Pattern Recognition.
So, first of all question rises in our mind is
WHAT IS PATTERN RECOGNITION?

4. What is pattern recognition?
“The assignment of a physical object or event to one of several prespecified categeries” -- Duda & Hart
A pattern is an object, process or event that can be given a name.
A pattern class (or category) is a set of patterns sharing common attributes and usually originating from the same source.
During recognition (or classification) given objects are assigned to prescribed classes.
A classifier is a machine which performs classification.

5. Examples of applications
Handwritten: sorting letters by postal code, input device for PDA‘s.
Printed texts: reading machines for blind people, digitalization of text documents.
Optical Character Recognition (OCR)
Biometrics
Diagnostic systems
Military applications
Face recognition, verification, retrieval.
Finger prints recognition.
Speech recognition.
Medical diagnosis: X-Ray, EKG analysis.
Machine diagnostics, waster detection.
Automated Target Recognition (ATR).
Image segmentation and analysis (recognition from aerial or satelite photographs).

6. About Signature Verification
Signature is an identification of a person through his/her hand writing. The recognition of human hand writing is a very important research area concerning with the improvement of the interface between the human beings and the computers. If the computer is intelligent enough to understand human hand writing it will provide a more attractive and more economic man computer interface with proper person authentication and attestation.

7. Difference from Character recognition
Signature verification is so different with the character recognition, because signature is often unreadable and it looks like an image with particular curves that represents the writing style of a person and not as a collection of letters and words. Signature is just a symbol and a special case of handwriting

8. Introduction:
Signature verification is a popular research area in the field of pattern recognition and document processing. It also plays an important role in many applications concerned with security, access control or financial and contractual matters.
In signature verification techniques comparison of signatures with variation in length and height is done, which occurs even for the repetition of the signature of one single person.

9. Types of Forgeries:
There are three types of forgeries taken into account.
Random Forgeries: Written by those person who don’t know the shape of original signature.
Simple Forgeries: Written by a person who knows the shape of original signature without much practice.
Skilled Forgeries: Written by a person who knows the shape with much practice of the signature.

10. Contd…
(Random)
(Original)
(Simple)
Skilled

11. Categories of Verification Technique:
Online Verification Approach: Also called as dynamic approach where signature is captured during the writing process. e.g. Building entrance, credit card processing etc.
Offline Verification Approach: Known as static approach where signature is captured once the writing process is over and only static image is available. E.g. Bank cheque clearing etc

12. Approaches
Statistical PR: based on underlying statistical model of patterns and pattern classes.
Structural (or syntactic) PR: pattern classes represented by means of formal structures as grammars, automata, strings, etc.
Neural networks: classifier is represented as a network of cells modeling neurons of the human brain (connectionist approach).

13. Neural Network
Neural Networks provide an emerging paradigm for image recognition implementation that involves large interconnected networks of relatively simple and typically nonlinear units.
There are three entities that characterize Neural Network
These are -
1. Network topology
2. characteristics of individual units
3. strategy for pattern learning

14. Contd: It include some more approaches also -
Simple Pattern Associators
Feedforward-with-backpropagation learning structure.
Hopfield method

15. Contd: Key Neural Network Concepts :
Overall Computational model consist of variable interconnection of simple elements.
Objective is for the network in the training process to develop an internal structure that enables it to correctly identify or classify new similar patterns.
Neural Networks are dynamic systems, whose state changes over time, in response to external inputs or an initial state.

16. Applications: {1} Banks (Mostly Uses)
{2} Performing Financial Transaction
{3} Boarding an Aircraft
{4} crossing international Borders

17. TOOLS USED: For making a system the tool which I am using is MATLAB.
It is more user friendly then any other language and very vast too.
Most important reason for using this tool is because MATLAB provides Pattern Recognition tool which helps me a lot while doing my project.

18. MAKING GUI FOR RESULT
By using MATLAB, I am creating a GUI which helps in reading the Signature and then checking for characters and curves of Signature.
I am making this interface for just to reduce the complexity of the system for the user.

19. Model:
The model that we are
going to use here is
Prototype Model.

20. References www.wikipedia.com
Y Santhosh Reddy, D Prasanna Babu “Novel Features for Off-line Signature Verification”
"An off-line signature verification usingHMMfor Random,Simple and Skilled Forgeries", Sixth International Conference on Document Analysis and Recognition, pp.