Abstract
This paper presents the development of an automatic method for classification of signatures according to a geometric shape recognition system based on a modular neural network (MNN). To carry out the method of automatic classification, we apply pre-processing to our database which consists of 30 individuals, first the image goes to a high pass filter, which allows the passage of signals depending on their frequency and finally we apply the Fourier transform, which is essentially a wave phenomenon which serves to measure the distribution of amplitudes of the frequency of our image (signatures), and presents to certain extent as they are, rise time, peak parking. Thus the signatures are automatically sorted to the module that corresponds in the Modular Neural Network, which contains three separate modules, each one uses different feature extraction methods: edge extraction, wavelet transform and Hough transform, where this results in the identification or recognition of signatures.
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Carrera, V., Melin, P., Bravo, D. (2013). Development of an Automatic Method for Classification of Signatures in a Recognition System Based on Modular Neural Networks. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Recent Advances on Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33021-6_16
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DOI: https://doi.org/10.1007/978-3-642-33021-6_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33020-9
Online ISBN: 978-3-642-33021-6
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