Efficient Handwritten Digit Recognition based on Histogram of Oriented Gradients and SVM
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10.5120/18229-9167 |
Reza Ebrahimzadeh and Mahdi Jampour. Article: Efficient Handwritten Digit Recognition based on Histogram of Oriented Gradients and SVM. International Journal of Computer Applications 104(9):10-13, October 2014. Full text available. BibTeX
@article{key:article,
author = {Reza Ebrahimzadeh and Mahdi Jampour},
title = {Article: Efficient Handwritten Digit Recognition based on Histogram of Oriented Gradients and SVM},
journal = {International Journal of Computer Applications},
year = {2014},
volume = {104},
number = {9},
pages = {10-13},
month = {October},
note = {Full text available}
}
Abstract
Automatic Handwritten Digits Recognition (HDR) is the process of interpreting handwritten digits by machines. There are several approaches for handwritten digits recognition. In this paper we have proposed an appearance feature-based approach which process data using Histogram of Oriented Gradients (HOG). HOG is a very efficient feature descriptor for handwritten digits which is stable on illumination variation because it is a gradient-based descriptor. Moreover, linear SVM has been employed as classifier which has better responses than polynomial, RBF and sigmoid kernels. We have analyzed our model on MNIST dataset and 97. 25% accuracy rate has been achieved which is comparable with the state of the art.
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