NDLI: Signature identification via local association of features

Content Provider	IEEE Xplore Digital Library
Author	Ke Han Sethi, I.K.
Copyright Year	1995
Description	Author affiliation: Dept. of Comput. Sci., Wayne State Univ., Detroit, MI, USA (Ke Han)
Abstract	Establishing the identify of a signature by automatic search through a database of signatures is of interest in several areas. This paper describes a system for this purpose. The proposed identification system uses a set of geometric and topologic features to characterize each signature. By considering the spatial distribution of these features, the system maps each signature into two strings of finite symbols. A local associative indexing scheme is then used on these strings to organize the collection of signatures of known identity. When presented with a signature of unknown identity, the system uses the same indexing scheme to retrieve a candidate set of signatures. A verification process is then carried out to find the best match from the candidate set. The performance of the proposed system has been tested with a moderate database. The results obtained indicate that the proposed system is able to identify signatures with great accuracy even when a part of a signature is missing.
Starting Page	187
Ending Page	190
File Size	627028
Page Count	4
File Format	PDF
ISBN	0818671289
DOI	10.1109/ICDAR.1995.598973
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-08-14
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Indexing System testing Neural networks Laboratories Computer science Spatial databases Focusing Handwriting recognition Business Law enforcement
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Writer identification of Arabic handwriting documents using grapheme features

Off-Line Signature Recognition and Verification Using Neural Network

A handwritten Chinese characters recognition method based on primitive and fuzzy features via SEART neural net model

Evaluation of holistic features for word recognition

A performance evaluation of a new signature verification algorithm using realistic forgeries

Use of dynamic features for signature verification

On-line Signature Verification: An Approach Based on Cluster Representations of Global Features

Recognition of free-form objects in dense range data using local features

HMM word recognition engine

Signature identification via local association of features

Similar Documents

Writer identification of Arabic handwriting documents using grapheme features

Off-Line Signature Recognition and Verification Using Neural Network

A handwritten Chinese characters recognition method based on primitive and fuzzy features via SEART neural net model

Evaluation of holistic features for word recognition

A performance evaluation of a new signature verification algorithm using realistic forgeries

Use of dynamic features for signature verification

On-line Signature Verification: An Approach Based on Cluster Representations of Global Features

Recognition of free-form objects in dense range data using local features

HMM word recognition engine

Signature identification via local association of features