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Título: Directional continuous wavelet transform applied to handwritten numerals recognition using neural networks
Autores: Romero, Diego
Seijas, Leticia
Ruedín, Ana M. C.
Fecha: 2008-05-21
2007
Publicador: Unversidad Nacional de La Plata
Fuente:


Tipo: Articulo
Articulo
Tema: neural networks; continuous wavelet transform; pattern recognition
Ciencias Informáticas
Redes neuronales
Wavelets y fractales
Reconocimiento de patrones
Informática
Descripción: The recognition of handwritten numerals has many important applications, such as automatic lecture of zip codes in post offices, and automatic lecture of numbers in checknotes. In this paper we present a preprocessing method for handwritten numerals recognition, based on a directional two dimensional continuous wavelet transform. The wavelet chosen is the Mexican hat. It is given a principal orientation by stretching one of its axes, and adding a rotation angle. The resulting transform has 4 parameters: scale, angle (orientation), and position (x,y) in the image. By fixing some of its parameters we obtain wavelet descriptors that form a feature vector for each digit image. We use these for the recognition of the handwritten numerals in the Concordia University data base. We input the preprocessed samples into a multilayer feed forward neural network, trained with backpropagation. Our results are promising.
Idioma: Inglés