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Kannada Handwritten Character Recognition Using Multi Feature Extraction Techniques

Journal: International Journal of Science and Research (IJSR) (Vol.3, No. 10)

Publication Date:

Authors : ; ; ;

Page : 911-916

Keywords : Character Segmentation; Optical Character Recognition; Hand Written Character Recognition; Optical Hand Character Recognition; Neural Network Classifier;

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In this paper, we have presented a method of feature extraction for handwritten character recognition. Handwritten character recognition is a complex task because of various writing styles of different individuals. Our Method yields good classification accuracy on handwritten characters, apart from complexity. Normalization and binarization are the pre-processing techniques used for getting accurate results of classification process in handwritten character recognition. To select a set of features is an important step for implementing a handwriting recognition system. In this work, we have extracted various features, namely-Hu's Invariant moments, Zernike moments, Zonal features and Fourier-Wavelet coefficients. The recognition process is carried out using Back Propagation Neural Network.

Last modified: 2021-06-30 21:10:56