Offline Signature Recognition and Verification System using Efficient Fuzzy Kohonen Clustering Network (EFKCN) Algorithm | Zendy

Dewi Suryani | Zendy; Edy Irwansyah | Zendy; Ricki Chindra | Zendy

Open Access

Offline Signature Recognition and Verification System using Efficient Fuzzy Kohonen Clustering Network (EFKCN) Algorithm

Author(s) -

Dewi Suryani,

Edy Irwansyah,

Ricki Chindra

Publication year - 2017

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2017.10.025

Subject(s) - computer science , normalization (sociology) , cluster analysis , signature recognition , signature (topology) , data mining , pattern recognition (psychology) , artificial intelligence , self organizing map , fuzzy clustering , database normalization , fuzzy logic , feature extraction , geometry , mathematics , sociology , anthropology

Research on offline signature recognition still has not shown satisfactory results as the results of recent research. Therefore this study aims to proposed an offline signature recognition and verification system which employed an efficient fuzzy Kohonen clustering networks (EFKCN) 1 algorithm. The proposed recognition system and signature verification system consist of five stages including data acquisition, image processing, data normalization, clustering, and evaluation. The recognition of signature patterns using the clustering method with the EFKCN algorithm shows relatively better result with 70% accuracy compared to the accuracy of previous research results 2 which is 53%, and a good signature recognition result can be developed to assist the verification system as well as the personal data verification system as made in this study.

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