A novel architecture to verify offline hand-written signatures using convolutional neural network
Hand-written signatures are marked on documents to establish legally binding evidence of identity and intent. However, they are prone to forgery, and the design of an accurate feature extractor to distinguish between highly-skilled forgeries and genuine signatures is a challenging task. In this pape...
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| Main Authors: | , , , , |
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| Format: | Conference Paper |
| Language: | English |
| Published: |
2020
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| Institution: | Universiti Tenaga Nasional |
| Language: | English |