Flexible Neural Network Architecture for Handwritten Signatures Recognition
Abstract
This article illustrates modeling of flexible neural networks for handwritten signatures preprocessing. An input signature is interpolated to adjust inclination angle, than descriptor vector is composed. This information is preprocessed in proposed flexible neural network architecture, in which some neurons are becoming crucial for recognition and adapt to classification purposes. Experimental research results are compared in benchmark tests with classic approach to discuss efficiency of proposed solution.
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International Journal of Electronics and Telecommunications
is a periodical of Electronics and Telecommunications Committee
of Polish Academy of Sciences
eISSN: 2300-1933