Multi-Script Off-line Signature Identification

Loading...
Thumbnail Image
File version
Author(s)
Pal, Srikanta
Alireza, Alaei
Pal, Umapada
Blumenstein, Michael
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)

Ajith Abraham

Date
2012
Size

495691 bytes

File type(s)

application/pdf

Location

Pune, India

License
Abstract

In this paper, we present an empirical contribution towards the understanding of multi-script signature identification. In the proposed signature identification system, the signatures of Bengali (Bangla), Hindi (Devanagari) and English are considered for the identification process. This system will identify whether a claimed signature belongs to the group of Bengali, Hindi or English signatures. Zernike Moment and histogram of gradient are employed as two different feature extraction techniques. In the proposed system, Support Vector Machines (SVMs) are considered as classifiers for signature identification. A database of 2100 Bangla signatures, 2100 Hindi signatures and 2100 English signatures are used for experimentation. Two different results based on two different feature sets are calculated and analysed. The highest accuracy of 92.14% is obtained based on the gradient features using 4200 (1400 Bangla +1400 Hindi + 1400 English) samples for training and 2100 (700 Bangla +700 Hindi +700 English) samples for testing.

Journal Title
Conference Title

Proceedings of the 2012 12th International Conference on Hybrid Intelligent Systems (HIS)

Book Title
Edition
Volume
Issue
Thesis Type
Degree Program
School
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement

© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Item Access Status
Note
Access the data
Related item(s)
Subject

Image Processing

Persistent link to this record
Citation