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- W2995338184 abstract "Handwritten Signature Recognition is an important behavioral biometric which is used for numerous identification and authentication applications. There are two fundamental methods of signature recognition, on-line or off-line. On-line recognition is a dynamic form, which uses parameters like writing pace, change in stylus direction and number of pen ups and pen downs during the writing of the signature. Off-line signature recognition is a static form where a signature is handled as an image and the author of the signature is predicted based on the features of the signature. The current method of Off-line Signature Recognition predominantly employs template matching, where a test image is compared with multiple specimen images to speculate the author of the signature. This takes up a lot of memory and has a higher time complexity. This paper proposes a method of off-line signature recognition using Convolution Neural Network. The purpose of this paper is to obtain high accuracy multi-class classification with a few training signature samples. Images are preprocessed to isolate the signature pixels from the background/noise pixels using a series of Image processing techniques. Initially, the system is trained with 27 genuine signatures of 10 different authors each. A Convolution Neural Network is used to predict a test signature belongs to which of the 10 given authors. Different public datasets are used to demonstrate effectiveness of the proposed solution." @default.
- W2995338184 created "2019-12-26" @default.
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- W2995338184 date "2018-12-01" @default.
- W2995338184 modified "2023-09-25" @default.
- W2995338184 title "Handwritten Signature Recognition: A Convolutional Neural Network Approach" @default.
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- W2995338184 doi "https://doi.org/10.1109/icacat.2018.8933575" @default.
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