vioft2nntf2t|tblJournal|Abstract_paper|0xf4ffa2d91d0000004640000001000600 In optical character recognition applications, the feature extraction method(s) used to recognize document images play an important role. The features are the properties of the pattern that can be statistical, structural and/or transforms or series expansion. The structural features are difficult to compute particularly from hand-printed images. The structure of the strokes present inside the hand-printed images can be estimated using statistical means. In this paper three features have been purposed, those are based on the distribution of B/W pixels on the neighborhood of a pixel in an image. We name these features as Spiral Neighbor Density, Layer Pixel Density and Ray Density. The recognition performance of these features has been compared with two more features Neighborhood Pixels Weight and Total Distances in Four Directions already studied in our work. We have used more than 20000 Devanagari handwritten character images for conducting experiments. The experiments are conducted with two classifiers i.e. PNN and k-NN.
Satish Kumar
Panjab University Swami Sarvanand Giri Regional Centre, India
Statistical Features, Hand-Printed Recognition, Devanagari Script, NPW (Neighborhood Pixels Weights), SND, LPD, RD, k-NN, PNN, Weighted Map
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 6 , Issue: 3 , Pages: 1159-1166 )
Date of Publication :
February 2016
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