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S.-W. Lee, C.-H. Kim, H. Ma and Y. Y. Tang, “Multiresolution Recognition of Unconstrained Handwritten Numerals with Wavelet Transform and Multilayer Cluster Neural Network,” Pattern Recognition, Vol. 29, No. 12, 1996, pp. 1953-1961. doi:10.1016/S0031-3203(96)00053-2

has been cited by the following article:

• TITLE: Handwritten Character Recognition Using Multiresolution Technique and Euclidean Distance Metric

  AUTHORS: Dileep Kumar Patel, Tanmoy Som, Sushil Kumar Yadav, Manoj Kumar Singh

  KEYWORDS: Discrete Wavelet Transform; Euclidean Distance Metric; Feature Extraction; Handwritten Character Recognition; Bounding Box; Mean Vector

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.3 No.2, May 30, 2012

  ABSTRACT: In the present paper, the problem of handwritten character recognition has been tackled with multiresolution technique using discrete wavelet transform (DWT) and Euclidean distance metric (EDM). The technique has been tested and found to be more accurate and faster. Characters is classified into 26 pattern classes based on appropriate properties. Features of the handwritten character images are extracted by DWT used with appropriate level of multiresolution technique, and then each pattern class is characterized by a mean vector. Distances from input pattern vector to all the mean vectors are computed by EDM. Minimum distance determines the class membership of input pattern vector. The proposed method provides good recognition accuracy of 90% for handwritten characters even with fewer samples.