Flexible Neural Network Architecture for Handwritten Signatures Recognition

Authors

  • Marcin Woźniak Institute of Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland
  • Dawid Polap Institute of Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland

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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Published

2016-06-20

Issue

Vol. 62 No. 2 (2016)

Section

Image Processing

License

Copyright (c) 2016 International Journal of Electronics and Telecommunications

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