Offline Signature Recognition using Hidden Markov Model (HMM)
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Abstract
Description
HMM has been used successfully to model speech and online
signature in the past two decades. The success has been attributed
to the fact that these biometric traits have time reference. Only
few HMM based offline signature recognition systems have be
developed because offline signature lack time reference. This
paper presents a recognition system for offline signatures using
Discrete Cosine Transform (DCT) and Hidden Markov Model
(HMM). The signature to be trained or recognized is vertically
divided into segments at the centre of gravity using the space
reference positions of the pixels. The number of segmented
signature blocks is equal to the number of states in the HMM for
each user notwithstanding the length of the signatures.
Experimental result shows that successful signatures recognition
rates of 99.2% is possible. The result is better in comparison with
previous related systems based on HMM and statistical classifiers.
Keywords
T Technology (General), TK Electrical engineering. Electronics Nuclear engineering