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Directional continuous wavelet transform applied to handwritten numerals recognition using neural networks
Keywords:
Neural Networks, Continuous Wavelet Transform, Pattern RecognitionAbstract
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.
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References
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[2] W. Pratt: Digital Image Processing, New York, Wiley, 1978.
[3] D. Gorgevik, D. Cakmakov: An Efficient Three-Stage Classifier for Handwritten Digit Recognition, Proceedings of the 17th International Conference on Pattern Recognition (ICPR’04), Vol.4, 2004, pp. 507-510.
[4] L. M. Seijas y E. C. Segura: Un clasificador neuronal que explica sus respuestas: aplicación al reconocimiento de dígitos manuscritos, Proceedings IX Congreso Argentino de Ciencias de la Computación (CACIC 2003), La Plata, Argentina, 2003.
[5] E. López-Rubio, J. Muñoz-Pérez, J. Gómez-Ruiz: A principal components analysis self-organizing map. Neural Networks, Vol. 17, No. 2, 2004, pp.261-270.
[6] B. Zhang, Fu, M., Yan, H.: A nonlinear neural network model of mixture of local principal component analysis: application to handwritten digits recognition, Pattern Recognition, Vol. 34, No. 2, 2001, pp. 203-214.
[7] Ana Ruedin: A Nonseparable multiwavelet for edge detection, Wavelet Appl. Signal Image Proc. X, Proc. SPIE, Vol. 5207, 2003, pp. 700-709.
[8] S. Liapis, G. Tziritas, Color and Texture Image Retrieval Using Chromaticity Histograms and Wavelet Frames, IEEE Transactions on Multimedia, Vol.6, No.5, 2004, pp. 676-686.
[9] G. Y. Chen, T. D. Bui and A. Krzyzak: Contour-Based Handwritten Numeral Recognition using Multiwavelets and Neural Networks, Pattern Recognition, Vol.36, No.7, 2003, pp.1597-1604.
[10] I. Daubechies: Ten lectures on wavelets, Society for Industrial and Applied Mathematics, 1992.
[11] S. Mallat: A Wavelet Tour of Signal Processing, Academic Press, 1999.
[12] A. Skodras, C. Christopoulos, T. Ebrahimi: JPEG2000: The upcoming still image compression standard, Elsevier Pattern Recognition Letters, Vol. 22, 2001, pp. 1337-1345.
[13] J.P. Antoine, P. Vandergheynst, K. Bouyoucef, R. Murenzi: Target detection and recognition using two-dimensional isotropic and anisotropic wavelets, Automatic Object Recognition V, SPIE Proc., 2485, 1995, pp. 20-31.
[14] J.P. Antoine, R. Murenzi: Two dimensional directional wavelets and the scale-angle representation, Signal Process. 53, 1996, pp. 259-281.
[15] L. Kaplan, R. Murenzi: Pose estimation of SAR imagery using the two dimensional continuous wavelet transform, Pattern Recognition Letters 24, 2003, pp. 22692280.
[16] J. Romero, L.Seijas, A. Ruedin: Reconocimiento de Dgitos Manuscritos Usando La Transformada Wavelet Continua en 2 Dimensiones y Redes Neuronales, XII Congreso Argentino de Ciencias de la Computacin CACIC 2006.
[17] J. Hertz, A. Krogh, R. Palmer: Introduction to the Theory of Neural Computation, Santa Fe Institute Editorial Board, 1990.
[18] S. Haykin: Neural Networks A Comprehensive Foundation, Prentice Hall, 1999.
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Published
2007-03-01
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[1]
“Directional continuous wavelet transform applied to handwritten numerals recognition using neural networks”, JCS&T, vol. 7, no. 01, pp. p. 66–71, Mar. 2007, Accessed: Jul. 08, 2025. [Online]. Available: https://journal.info.unlp.edu.ar/JCST/article/view/805
