316,254 research outputs found
Handwritten Signature Verification using Deep Learning
Every person has his/her own unique signature that is used mainly for the purposes of personal
identification and verification of important documents or legal transactions. There are two kinds of signature
verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document
signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her
signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a large
number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in
online biometric personal verification such as fingerprints, eye scan etc. In this paper we created CNN model
using python for offline signature and after training and validating, the accuracy of testing was 99.70%
Digital implementation of the cellular sensor-computers
Two different kinds of cellular sensor-processor architectures are used nowadays in various
applications. The first is the traditional sensor-processor architecture, where the sensor and the
processor arrays are mapped into each other. The second is the foveal architecture, in which a
small active fovea is navigating in a large sensor array. This second architecture is introduced
and compared here. Both of these architectures can be implemented with analog and digital
processor arrays. The efficiency of the different implementation types, depending on the used
CMOS technology, is analyzed. It turned out, that the finer the technology is, the better to use
digital implementation rather than analog
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