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A Character Based Handwritten Identification Using Neural Network and SVM

Authors(2):

Rohitash Kumar, Mandeep Kaur
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Handwritten identification is carried out using handwritten text. The Research work on investigates highly discriminating features for handwritten identification for off-line handwritten multiple text lines and passages. Five categories of features are tested: slant and slant energy, skew, pixel distribution, curvature, and entropy. These features support high recognition rates and are competitive with other state of the art methods for handwritten identification. The directional element features are first extracted from the handwriting character scripts, then the dimensions of the features is reduced using PCA in order to cope with the small sample size problem. The most discriminative features are extracted from the reduced feature space using Fisher's Linear Discriminant Analysis. The Euclidian distance is Research for classification. Experimental result using SVM and NN verified the effectiveness of the Research work. For the implementation of this Research work we use the Image Processing Toolbox under MATLAB software.

Rohitash Kumar, Mandeep Kaur

PCA, SVM, MATLAB, DEF, PCA, LDA, IAM, ICDAR, GUI, CCR, PSNR

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Publication Details

Published in : Volume 3 | Issue 1 | January-February - 2017
Date of Publication Print ISSN Online ISSN
2017-02-28 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
120-124 IJSRSET173120   Technoscience Academy

Cite This Article

Rohitash Kumar, Mandeep Kaur, "A Character Based Handwritten Identification Using Neural Network and SVM", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 1, pp.120-124, January-February-2017.
URL : http://ijsrset.com/IJSRSET173120.php

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