Handwritten Gurmukhi Numeral Recognition using Different Feature Sets

there is an emerging trend in the research to recognize handwritten characters and numerals of many Indian languages and scripts. In this manuscript we have practiced the recognition of handwritten Gurmukhi numerals. We have used three different feature sets. First feature set is comprised of distance profiles having 128 features. Second feature set is comprised of different types of projection histograms having 190 features. Third feature set is comprised of zonal density and Background Directional Distribution (BDD) forming 144 features. The SVM classifier with RBF (Radial Basis Function) kernel is used for classification. We have obtained the 5-fold cross validation accuracy as 99.2% using second feature set consisting of 190 projection histogram features. On third and first feature sets recognition rates 99.13% and 98% are observed. To obtain better results pre-processing of noise removal and normalization processes before feature extraction are recommended, which are also practiced in our approach.

[1]  Renu Dhir,et al.  Performance Comparison of Devanagari Handwritten Numerals Recognition , 2011 .

[2]  Tetsushi Wakabayashi,et al.  Handwritten Numeral Recognition of Six Popular Indian Scripts , 2007, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007).

[3]  Dharam Veer Sharma,et al.  Recognition of Isolated Handwritten Characters of Gurumukhi Script using Neocognitron , 2010 .

[4]  Santanu Chaudhury,et al.  Devnagari numeral recognition by combining decision of multiple connectionist classifiers , 2002 .

[5]  Apurva A. Desai,et al.  Gujarati handwritten numeral optical character reorganization through neural network , 2010, Pattern Recognit..

[6]  T. R. Sontakke,et al.  Rotation, scale and translation invariant handwritten Devanagari numeral character recognition using general fuzzy neural network , 2007, Pattern Recognit..

[7]  Gurpreet Singh Lehal,et al.  Digit extraction and recognition from machine printed Gurmukhi documents , 2009, MOCR '09.

[8]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[9]  Ujjwal Bhattacharya,et al.  Neural Combination of ANN and HMM for Handwritten Devanagari Numeral Recognition , 2006 .

[10]  K. Siddharth,et al.  Handwritten Gurmukhi Character Recognition Using Statistical and Background Directional Distribution Features , 2011 .

[11]  Navneet Kaur,et al.  Recognition of Handwritten Devanagari Numerals , 2013 .

[12]  Bidyut Baran Chaudhuri,et al.  A majority voting scheme for multiresolution recognition of handprinted numerals , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[13]  G. G. Rajput,et al.  Fourier Descriptor based Isolated Marathi Handwritten Numeral Recognition , 2010 .

[14]  Gurpreet Singh Lehal,et al.  A Recognition System for Devnagri and English Handwritten Numerals , 2000, ICMI.

[15]  Sanjay S. Gharde,et al.  Support Vector Machine for Handwritten Devanagari Numeral Recognition , 2010 .