it's a signature verification project, where the signature is verified from our dataset and matched with the current one. If it matches the test case then the process is verified and if it doesn't then the process repeats or it depends on user, whether he/she want to continue the process or not.

1. Signature Verification
Using CNN
Submitted By:
Rachana Kulkarni (3747)
Sayali Kawale(3753)
Rifa Shazmeen(3755)

2. Introduction
 Computers are largely used in almost each and every field including financial
transactions, thus providing additional security measures for computer system is
necessary.
 The security measures to be used must be cheap, reliable and un-intrusive to the
authorized person. The technique which meets these requirements is handwritten
signature verification.

3. Why Handwritten Signature only ?
 Everyday transaction
 Individuals less likely to object
 Individual’s signature is unique
 Less controversial

4. Importance of Signature
 Transaction
 Individuals less likely to object
 Biometric

5. Types of Signature Identification
 Offline Signature Verification
 Deals with shape only
 Online Signature Verification
 Deals with dynamic features like speed, pen pressure, directions, stroke length, and
when the pen is lifted from the paper

6. Challenging tasks
 The main challenge with online Signature Identification technology is
differentiating between the consistent parts of the signature and the behavioural
parts of the signature that vary with each signing.
 A person’s signature is never entirely the same. Allowing for these variations while
providing protection against forgers is a challenging task.
 Differentiating between the parts of the signature that vary with each signing.
 The signature can vary substantially over an individual’s lifetime.

7. Proposed Work
 Signature Acquisition
 Signature Pre-processing
 Feature Extraction
 Signature Verification

8. Signature Acquisition
 Signature is acquired from the user

9. Signature Pre-processing
 Function of the pre-processor
• Image resizing
• Image binarizing
• Image thinning
• Image normalizing

10. Feature Extraction

11. Signature Identification

12. Applications
 Banking
 Passport office
 And any other places which require identification !

13. Advantages
 Low error rate
 Forgery is detected even when the forger has managed to get a copy of the
authentic signature
 Fast and simple training
 Cheap hardware
 Little storage requirements

14. Activity Diagram

15. Requirements & Technologies
 The hardware component we are using here is a scanning device(WEBCAM), high
RAM for better results and good processor.
 Operating System : Windows XP or Higher
 NetBeans IDE 7.0
 SDK – J2SE
 Intel core 2 duo processor
 2.1 GHZ, 1 GB RAM