Work place: Department of Electronics & Instrumentation Engg Chhatrapati Shivaji Institute of Technology, Durg, Chhattisgarh, India
Research Interests: Information-Theoretic Security, Digital Library, Network Security, Information Security
Shailendra Kumar Dewangan received B.E. in Electronics & Telecommunication in year 2005 and M.E. in Communication Engineering from Shri Shankaracharya College of Engineering & Technology (SSCET), Bhilai, Chhattisgarh, India. He is currently working as an Assistant Professor in the Department of Electronics & Instrumentation Engineering at Chhatrapati Shivaji Institute of Technology (CSIT), Durg, Chhattisgarh, India. His areas of interest include Digital Signal Processing, information security, digital watermarking, advancements in communication technology, etc. He has published multiple articles in various International and National Journals & Conferences. Besides he has lifetime membership of Indian Society of Technical Education (ISTE) and Associate membership of Institute of Electronics & Telecommunication Engineers (IETE).
DOI: https://doi.org/10.5815/ijigsp.2013.04.04, Pub. Date: 8 Apr. 2013
Handwritten signatures are the most commonly used method for authentication of a person as compared to other biometric authentication methods. For this purpose Neural Networks (NN) can be applied in the process of verification of handwritten signatures that are electronically captured. This paper presents a real time or online method for recognition and verification handwritten signatures by using NN architecture. Various features of signature such as height, length, slant, Hu's moments etc are extracted and used for training of the NN. The objective of online signature verification is to decide, whether a signature originates from a given signer. This recognition and verification process is based on the instant signature image obtained from the genuine signer and a few images of the original signatures which are already part reference database. The process of Devnagari signature verification can be divided it into sub-processes as pre-processing, feature extraction, feature matching, feature comparison and classification. This stepwise analysis allows us to gain a better control over the precision of different components[...] Read more.
Subscribe to receive issue release notifications and newsletters from MECS Press journals