Kannada Handwritten Character Recognition Using Multi Feature Extraction Techniques
Aravinda.C.V, Dr. H. N.Prakash, Lavanya S
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.
Keywords: Character Segmentation, Optical Character Recognition, Hand Written Character Recognition, Optical Hand Character Recognition, Neural Network Classifier
Edition: Volume 3 Issue 10, October 2014
Pages: 911 - 916