Mahesh Jangid (2011) Devanagari Isolated Character Recognition by using Statistical features ( Foreground Pixels Distribution, Zone Density and Background Directional Distribution feature and SVM Classifier)

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Devanagari Isolated Character Recognition by using Statistical features ( Foreground Pixels Distribution, Zone Density and Background Directional Distribution feature and SVM Classifier)

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CONTRIBUTORS:

Author:

Mahesh Jangid

JOURNAL:

International Journal on Computer Science and Engineering (IJCSE), 3(6), 2400 - 2407.

YEAR:

2011

PUB TYPE:

Journal Article

SUBJECT(S):

Davanagari Character Recognition, Forground pixel, Zone density, Background directional distribution, Support Vector Machine

DISCIPLINE:

Computer Science

HTTP:

http://www.enggjournals.com/ijcse/doc/IJCSE11-03-06-104.pdf

LANGUAGE:

English

PUB ID:

103-489-239 (Last edited on 2011/06/19 00:49:21 GMT-6)

SPONSOR(S):

ABSTRACT:
In this paper, we present a methodology for off-line Isolated handwritten Devanagari character recognition. The proposed methodology relies on a three feature extraction techniques. The first technique is based on recursive subdivisions of the character image so that the resulting sub-images at each iteration have balanced (approximately equal) numbers of foreground pixels, as far as this is possible. Second technique is based on the zone density of the pixel and third is based on the directional distribution of neighboring background pixels to foreground pixels. The 314 sized feature vector is form from the three feature extraction techniques for a handwritten Devanagari character. The dataset (12240 samples) of handwritten Devanagari Character, have been prepared by writing the different – 2 people who belongs to different age group and obtained the 94.89 % recognition accuracy.

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