DocumentCode :
3539946
Title :
A survey for handwritten signature verification
Author :
Sanmorino, Ahmad ; Yazid, Setiadi
Author_Institution :
Fac. of Comput. Sci., Univ. Indonesia, Depok, Indonesia
fYear :
2012
fDate :
14-15 Aug. 2012
Firstpage :
54
Lastpage :
57
Abstract :
Signature verification is the process used to recognize an individual´s handwritten signature. Signature verification can be divided into two main areas depending on the data acquisition method, off-line and on-line signature verification. In this paper we attempt to survey the signature verification based on three categories. First, judging from how to get the data signature which is off-line and on-line verification. Second, based on the technique used, that is rule-based approach, neural networks, hidden Markov model and support vector machine. Third, based on preprocessing and feature extraction, which is thinning and line segmentation. Based on the survey, it was concluded that any method of verification has advantages and disadvantages. However, if viewed from the ease of implementation and performance, using neural networks or hidden Markov models are the right choice. Depending on the data acquisition method, on-line verification is recommended to use than off-line verification.
Keywords :
data acquisition; feature extraction; handwriting recognition; hidden Markov models; image segmentation; image thinning; knowledge based systems; neural nets; data acquisition method; feature extraction; handwritten signature recognition; hidden Markov model; line segmentation; neural network; offline handwritten signature verification; online handwritten signature verification; rule-based approach; support vector machine; thinning; Artificial neural networks; Feature extraction; Handwriting recognition; Hidden Markov models; Iris recognition; Handwritten Signature; Hidden Markov Model; Line Segmentation; Neural Networks; Off-line Verification; On-line Verification; Rule-Based; Support Vector Machine; Thinning; Verification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Uncertainty Reasoning and Knowledge Engineering (URKE), 2012 2nd International Conference on
Conference_Location :
Jalarta
Print_ISBN :
978-1-4673-1459-6
Type :
conf
DOI :
10.1109/URKE.2012.6319582
Filename :
6319582
Link To Document :
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