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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Pottier, I. Burel, G. |
| Copyright Year | 1994 |
| Description | Author affiliation: Thomson-CSF, Cesson-Sevigne, France (Pottier, I.; Burel, G.) |
| Abstract | TCSF/LER has developed an automatic system to identify or verify off-line handwritten signatures, using a connectionist approach. The authors' method combines image processing which consists in extracting significant parameters from the signature image and classification by a multilayer perceptron which uses the previous parameters as input. In this paper, the image processing step is described according to the intrinsic features of handwriting. Then, the proposed neural networks are compared with others classifiers including pseudo-inverse, k-nearest-neighbours and k-means and the influence of pre-processing and bad segmentation is measured. On a base of around fifty signers (comprising English, French signatures and paraphes), many experimental results are given for identification and verification purposes. |
| Starting Page | 2948 |
| Ending Page | 2951 |
| File Size | 273968 |
| Page Count | 4 |
| File Format | |
| ISBN | 078031901X |
| DOI | 10.1109/ICNN.1994.374701 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1994-06-28 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image processing Writing Neural networks Image recognition Handwriting recognition Application software Image storage Shape Image segmentation Access control |
| Content Type | Text |
| Resource Type | Article |
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