by Sunil Kumar Dyavaranahalli Sannappa 1,* 
, Kiran 2 , Sudheesh Kannur Vasudeva Rao 2 , Yashwanth Jagadeesh 2
1 Department of Computer Science, Mangalore University, Mangalore, India-574199
2 Department of ECE, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India-570002
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 1, Issue 5, Page # 160-166, 2022; DOI: 10.55708/js0105017
Keywords: Signature, Recognition, SVM, Forgery, Genuine
Received: 07 January 2022, Accepted: 04 May 2022, Published Online: 25 May 2022
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Investigation on verification of offline signature has explored a huge sort of techniques on more than one signature datasets, which can be amassed beneath managed conditions. However, these records will not necessarily reflect the characteristics of the signatures in some useful use cases. In this work, introduced a novel feature representation technique called edge histogram and 4 directional histograms for offline signature verification system. For classification of signature support vector machine (SVM) technique employed. Edge is a curve or point where the intensity of an image changes rapidly. Edges represent the boundary of object of an image. Edge detection is a process of detecting edges of an image. Several algorithms are available to detect edges effectively from an image. Canny, Roberts, Prewitt and Sobel are several popular available edge detectors.
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