Efficient Handwritten Digit Recognition based on Histogram of Oriented Gradients and SVM

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. General Terms Image Processing, Computer Vision, Artificial Intelligence

[1]  Michael E. Tipping.  The Relevance Vector Machine , 1999, NIPS.

[2]  Ângelo Cardoso,et al.  Handwritten digit recognition using biologically inspired features , 2013, Neurocomputing.

[3]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[4]  Jiliu Zhou,et al.  Incremental Tensor Principal Component Analysis for Handwritten Digit Recognition , 2014 .

[5]  Yann LeCun,et al.  Transformation Invariance in Pattern Recognition-Tangent Distance and Tangent Propagation , 1996, Neural Networks: Tricks of the Trade.

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

[7]  Sun Zhen,et al.  Recognition Method for Handwritten Digits Based on Improved Chain Code Histogram Feature , 2013, ICMT 2013.

[8]  Yann LeCun,et al.  Efficient Pattern Recognition Using a New Transformation Distance , 1992, NIPS.

[9]  Yann LeCun,et al.  Transformation Invariance in Pattern Recognition - Tangent Distance and Tangent Propagation , 2012, Neural Networks: Tricks of the Trade.

[10]  Bernhard Schölkopf,et al.  Prior Knowledge in Support Vector Kernels , 1997, NIPS.

[11]  Mahantapas Kundu,et al.  A genetic algorithm based region sampling for selection of local features in handwritten digit recognition application , 2012, Appl. Soft Comput..

[12]  Bernhard Schölkopf,et al.  Extracting Support Data for a Given Task , 1995, KDD.

[13]  M. Tech,et al.  Employing Neocognitron Neural Network Base Ensemble Classifiers To Enhance Efficiency Of Classification In Handwritten Digit Datasets , 2011 .

[14]  M. Narasimha Murty,et al.  An Application: Handwritten Digit Recognition , 2011 .

[15]  Ching Y. Suen,et al.  A novel hybrid CNN-SVM classifier for recognizing handwritten digits , 2012, Pattern Recognit..