Backpropagation and Its Application to Handwritten Signature Verification
Abstract
A pool of handwritten signatures is used to train a neural net(cid:173) work for the task of deciding whether or not a given signature is a forgery. The network is a feedforward net, with a binary image as input. There is a hidden layer, with a single unit output layer. The weights are adjusted according to the backpropagation algorithm. The signatures are entered into a C software program through the use of a Datacopy Electronic Digitizing Camera. The binary signa(cid:173) tures are normalized and centered. The performance is examined as a function of the training set and network structure. The best scores are on the order of 2% true signature rejection with 2-4% false signature acceptance.
Cite
Text
Wilkinson et al. "Backpropagation and Its Application to Handwritten Signature Verification." Neural Information Processing Systems, 1988.Markdown
[Wilkinson et al. "Backpropagation and Its Application to Handwritten Signature Verification." Neural Information Processing Systems, 1988.](https://mlanthology.org/neurips/1988/wilkinson1988neurips-backpropagation/)BibTeX
@inproceedings{wilkinson1988neurips-backpropagation,
title = {{Backpropagation and Its Application to Handwritten Signature Verification}},
author = {Wilkinson, Timothy S. and Mighell, Dorothy A. and Goodman, Joseph W.},
booktitle = {Neural Information Processing Systems},
year = {1988},
pages = {340-347},
url = {https://mlanthology.org/neurips/1988/wilkinson1988neurips-backpropagation/}
}