Online Handwritten Signature Recognition by Length Normalization using Up-Sampling and Down-Sampling

Online Handwritten Signature Recognition by Principal Components and Support Vector Machine.

“…types of preprocessing techniques are filtering, noise smoothing reduction, signature re-sampling. Such as online handwritten signature recognition by length normalization using Up-Sampling and Down-Sampling is proposed in [7], here normalization is based on interpolation operation for those samples which are less than set threshold length and down-sampling for those signature samples for more than threshold. It is essential to mention that proposed signature verification is depended on this normalization as in [7].…”

“…In function features the signature is characterized as a time series signals [7], for instance, horizontal signal…”

“…Hidden Markov Models (HMMs) is also used for online signature verification as in [11], Support Vector Machine (SVM) [8], Bayesian decision method [12]. [t p sampled at time t. the operation starts by doing normalization operation to the all signature samples according to the down-sampling and up-sampling operation to make all the signature samples as a similar length, the complete work is detailed in [7]. In this experiment, the length is chosen to be 256 trajectories as the average length of the SIGMA database [13] samples.…”

Empirical mode decomposition for online handwritten signature verification

“…To perform this operation, we have endeavouring different techniques of interpolation such as the linear and the polynomial interpolation, and for the reason of its simplicity and success in the case of two variables the bilinear interpolation [27] is also adopted. This latter, can be interpreted as a succession of two linear interpolations, one in each direction [28]. The reference signature length is chosen as the reference length of interpolation and all the other signatures are interpolated to have the same number of points as the reference one.…”

Off-Line Arabic (Indian) Numbers Recognition Using Expert System

“…Normally, in the verification or identification comparison, there are two possible error measures: False Accept Rate (FAR), which results from the forged template that accepted by the computer system falsely during testing. and False Rejection Rate (FRR), which results from the genuine template that the system recognizes as the query template wrongly [29,30]. Finally, the total accuracy of the system is calculated by subtracting the average error rate from 100% as in (18): (18) In this research, FAR error does not exist, as there are no forge templates in this experiment.…”