Ding, Jimmy Jia Kang (2026) Offline signature verification. Final Year Project, UTAR.
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Abstract
Handwritten signature remains a widely used behavioural biometric, supporting the importance of a robust offline signature verification. However, offline signature verification remains difficult due to high intra-class variability, high inter-class similarity, poor computational efficiency, and the imbalance of real-world training data. This project proposed a novel Convolutional Neural Network (CNN)-based model enhanced with
| Item Type: | Final Year Project / Dissertation / Thesis (Final Year Project) |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Faculty of Information and Communication Technology > Bachelor of Computer Science (Honours) |
| Depositing User: | ML Main Library |
| Date Deposited: | 06 Aug 2026 18:43 |
| Last Modified: | 06 Aug 2026 18:43 |
| URI: | http://eprints.utar.edu.my/id/eprint/7760 |
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