1) The document describes a project on signature verification that aims to classify input signatures as genuine or forgeries by matching them to database signatures. 2) It outlines the objectives, methodology, system architecture, and hardware/software requirements which include preprocessing signatures, extracting features, and training a model for verification. 3) The proposed approach uses simple signature matching through Python and PyCharm to provide a language-independent verification with advantages of low cost, little storage needs, and ability to detect forgeries even from signature copies.

1. VERIFICATION OF SIGNATURE
Project Members 1) Sakshi Jadhav
2) Nikita Jadhav
3) Vaishnavi Sonawane
4) Neha Pagariya
Guided By: A. A. Pawar Sir
Hod: Prof . P. G. Sali sir
Project Co-Ordinator: Ms . S. S. Shinde Mam

2. AIM :-
• The aim of signature verification is to classify the input
signature as genuine or forgery by matching it against the
database signature image using some distance measure.
• Forgery means that an individual is trying to make false
signatures of any other individual to become authenticated.

3. OBJECTIVES :-
• Information about the way the human hand creates the
signature such as hand speed and pressure measurements,
acquired from special peripheral units, is needed.
• A signature recognition and verification is a system capable of
efficiently addressing two individual but strongly related tasks
identification of the signature owner, and , decision whether the
signature is genuine or forger.

4. ABSTRACT:-
Signatures are widely used as a means of personal
identification and verification. Many documents like bank cheques
and legal transactions require signature verification. Signature-
based verification of a large number of documents is a very difficult
and time consuming task . Consequently an explosive growth has
been observed in biometric personal verification and authentication
systems that are connected with quantifiable physical unique
characteristics (finger prints, hand geometry, face, ear, iris scan
Verifying a signature will tell you if the signed data has changed or
not. When a digital signature is verified, the signature is decrypted
to produce the original value.

5. INTRODUCTION:-
The authenticity of many legal, financial, and other documents is
done by the presence or absence of an authorized handwritten
signature. “Digital Signature” is the best solution for authenticity
in various fields. A digital signature is nothing but an attachment
to any piece of Python Based information, which represents the
content of the document for that document. Now days , many
fraud things happens if any unknown person wants to imitate
person’s identity. If a person sign name of the checking account
holder to check without account holder’s permission, then this is
considered signature forgery

6. LITERATURE SURVEY:-
Sr No Paper Name
Year of
Publication
1) A New Approach of Digital
Signature Verification based on
BioGamal Algorithm
2019
2) Signature verification &
Recognization Case Study
2019

7. SYSTEM ARCHITECTURE:-

8. METHODOLOGY:-
• In this project, Before training all signatures image are passed through the
preprocessing stage. In preprocessing stage images are captured which are mostly
horizontally there can be variations in size , diversion , thickness of line
,background paper color etc. Preprocessing signature images are fed to the. In this
project, Before training all signatures image are passed through the Preprocess ing
stage. In preprocessing stage images are captured which are mostly horizontally
there can be variations in size , diversion , thickness of line ,background paper
color etc.
• The offline image and online data of the signature are simultaneously obtained
through the smart pen, and then the quality of the signature data is improved
through preprocessing and feature extraction to ensure the accuracy of the
verification result. Each signaturehas a corresponding offline image and online
data. The online curve in the table is obtained based on the X coordinate of the
signature data. The abscissa of the graph represents time, and the ordinate
represents the change of the X coordinate or Y coordinate of the online signature
over time during the writing processAfter applying the feature extraction process
the test

9. Er Diagram:-

10. DFD Diagram Level 0 :-

11. DFD Level 1 Diagram :-

12. HARDWARE REQUIREMENT:-
• Laptop or PC
 Windows 7 or higher
 I5 processor system or higher
 8 GB RAM or higher
 100 GB ROM or higher
 50 GB SSD

13. Software Requirement:-
i. Laptop or PC
• Python :
1. PyCharm 64

14. ADVANTAGE:-
• Signature is a man-made biometric system where forgery has
been studied extensively.
• Forgery is detected even when the forger has managed to get a copy
of the authentic signature.
• The signature verification system is independent of the native
language user.
• Cheap hardware.
• Little storage requirements.

15. DISADVANTAGE:-
• The private key must be kept in a secured manner.
• The process of generation and verification of digital signature
requires considerable amount of time.

16. CONCLUSION:-
In this project, simple and effective Signature Matching-
based language-independent signature verification architecture
has been purposed. The purposed model is quite simple of
signature matching using python using PyCharm 64So far we
have figured out several problems of existing methods of
signature detection through implementation. Still we did not
implemented our proposed method but from the implementation
and analysis of existing method we can say that it will give us
better performance.

17. REFERENCES:-
• Devnath , L. & Islam, Md. R. (2016). Off-line human signature recognition system
based on histogram analysis using MATLAB.
• Kumar, D. A. & Dhandapani, S. (2016). A novel bank check signature verification
model using concentric circle masking features and its performance analysis over
various neural network training functions.
• Salama, M. A. & Hussein, W, (2016). Invariant directional feature extraction and
matching approach for robust offLine signature verification.