Directional continuous wavelet transform applied to handwritten numerals recognition using neural networks

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 dimensiona...

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Detalles Bibliográficos
Autores principales: Romero, Diego J., Seijas, Leticia, Ruedín, Ana M. C.
Formato: Articulo
Lenguaje:Inglés
Publicado: 2007
Materias:
Ciencias Informáticas
continuous wavelet transform
Neural nets
PATTERN RECOGNITION
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/9530
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Mar07-11.pdf
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-9530
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
continuous wavelet transform
Neural nets
PATTERN RECOGNITION
spellingShingle Ciencias Informáticas
continuous wavelet transform
Neural nets
PATTERN RECOGNITION
Romero, Diego J.
Seijas, Leticia
Ruedín, Ana M. C.
Directional continuous wavelet transform applied to handwritten numerals recognition using neural networks
topic_facet Ciencias Informáticas
continuous wavelet transform
Neural nets
PATTERN RECOGNITION
description 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.
format Articulo
Articulo
author Romero, Diego J.
Seijas, Leticia
Ruedín, Ana M. C.
author_facet Romero, Diego J.
Seijas, Leticia
Ruedín, Ana M. C.
author_sort Romero, Diego J.
title Directional continuous wavelet transform applied to handwritten numerals recognition using neural networks
title_short Directional continuous wavelet transform applied to handwritten numerals recognition using neural networks
title_full Directional continuous wavelet transform applied to handwritten numerals recognition using neural networks
title_fullStr Directional continuous wavelet transform applied to handwritten numerals recognition using neural networks
title_full_unstemmed Directional continuous wavelet transform applied to handwritten numerals recognition using neural networks
title_sort directional continuous wavelet transform applied to handwritten numerals recognition using neural networks
publishDate 2007
url http://sedici.unlp.edu.ar/handle/10915/9530
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Mar07-11.pdf
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AT seijasleticia directionalcontinuouswavelettransformappliedtohandwrittennumeralsrecognitionusingneuralnetworks
AT ruedinanamc directionalcontinuouswavelettransformappliedtohandwrittennumeralsrecognitionusingneuralnetworks
bdutipo_str Repositorios
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