Online Signature Recognition Using CNN &RNN
| dc.contributor.author | Ekram Othman | |
| dc.contributor.author | Maram Bani jabber | |
| dc.date.accessioned | 2024-01-16T11:25:07Z | |
| dc.date.available | 2024-01-16T11:25:07Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Signature is widely used in human daily lives, and serves as a supplementary characteristic for verifying human identity. However, there is rare work of verifying signature. In this paper, we propose a few deep learning architectures to tackle this task, ranging from CNN, RNN to CNN-RNN compact model. We also improve Path Signature Features by encoding temporal information in order to enlarge the discrepancy between genuine and forgery signatures. Our numerical experiments demonstrate the effectiveness of our constructed models and features representations, also the experimental results indicate significant error reduction and accuracy enhancement in comparison with state of the art counterparts. | |
| dc.identifier.uri | https://hdl.handle.net/20.500.11888/18605 | |
| dc.supervisor | Dr.Khadija Mayyaleh | |
| dc.title | Online Signature Recognition Using CNN &RNN |