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

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.

[1]  Gérard Dreyfus,et al.  Single-layer learning revisited: a stepwise procedure for building and training a neural network , 1989, NATO Neurocomputing.

[2]  Vanita Mane,et al.  Handwritten character recognition using elastic matching and PCA , 2009, ICAC3 '09.

[3]  Latesh G. Malik,et al.  Fine Classification & Recognition of Hand Written Devnagari Characters with Regular Expressions & Minimum Edit Distance Method , 2008, J. Comput..

[4]  Ulrich H.-G. Kreßel,et al.  Pairwise classification and support vector machines , 1999 .

[5]  Mahantapas Kundu,et al.  Recognition of Non-Compound Handwritten Devnagari Characters using a Combination of MLP and Minimum Edit Distance , 2010, ArXiv.

[6]  Bidyut Baran Chaudhuri,et al.  Indian script character recognition: a survey , 2004, Pattern Recognit..

[7]  Fumitaka Kimura,et al.  Recognition of Off-Line Handwritten Devnagari Characters Using Quadratic Classifier , 2006, ICVGIP.

[8]  Tetsushi Wakabayashi,et al.  Off-Line Handwritten Character Recognition of Devnagari Script , 2007, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007).

[9]  Satish Kumar,et al.  Performance Comparison of Features on Devanagari Hand-printed Dataset , 2009 .

[10]  Mita Nasipuri,et al.  A Two Stage Classification Approach for Handwritten Devanagari Characters , 2010, ArXiv.

[11]  Tetsushi Wakabayashi,et al.  Accuracy Improvement of Devnagari Character Recognition Combining SVM and MQDF , 2008 .

[12]  R. Mahesh K. Sinha,et al.  A Journey from Indian Scripts Processing to Indian Language Processing , 2009, IEEE Annals of the History of Computing.

[13]  Mita Nasipuri,et al.  A Two Stage Classification Approach for Handwritten Devnagari Characters , 2010, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).