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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|Published in :
||Volume 3 | Issue 1 | January-February - 2017
|Date of Publication
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