Signature as a generally accepted way of consent and authorization plays an important role in social life. According features of off-line handwritten Chinese signature, utilized Support Vector Machine multiple classifiers which were constru-cted by extracting signature features combination method, and through decision module based on data fusion implemented off-line handwritten Chinese signature identification. Experiments show that the signature identification effect has been greatly im-proved. © 2012 Springer-Verlag GmbH.
CITATION STYLE
Zhang, S., & Li, F. (2012). Off-line handwritten Chinese signature verification based on support vector machine multiple classifiers. In Lecture Notes in Electrical Engineering (Vol. 155 LNEE, pp. 563–568). https://doi.org/10.1007/978-3-642-28744-2_74
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