Support Vector Machine for Handwritten Numeral Recognition

This book is beneficial for those researchers who are working on Mahine Learning techniques, Support Vector Machine, Handwritten Characters of any language or Pattern Recognition. Handwritten character recognition is one of the application domains in pattern classification. Recognition of Handwritten Devanagari Numerals/ Characters is a complicated task due to the unconstrained shape variations, different writing styles and different kinds of noise. Also, handwriting depends much on the writer and because one does not always write the same digit in exactly the same way. Support Vector Machine is one of the better classifier among all Machine Learning algorithms for pattern recognition. Most researchers have applied it on English, Persian, Chinese, Arabic and Tamil characters as well as numerals and acquired better recognition rate. For extracting features from each sample, the hybrid approach of Moment Invariant and Affine Moment Invariant has been adopted. Overall 18 features corresponding to each numeral proceed for classification using Support Vector Machine classifier. The recognition rate of this method is 99.48%.

Autorentext

Sanjay S. Gharde, Assistant Professor in Computer Engineering Department at Shrama Sadhana Bombay Trust's College of Engineering and Technology, Jalgaon (India). Areas of interest are Handwritten Character Recognition, Support Vector Machine. Twenty-Three research papers and five textbooks are on his credit. 11 years professional experience.