Efficient Handwritten Digit Recognition based on Histogram of Oriented Gradients and SVM
Reza Ebrahimzadeh,Mahdi Jampour +1 more
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TLDR
This paper has proposed an appearance feature-based approach which process data using Histogram of Oriented Gradients (HOG), a very efficient feature descriptor for handwritten digits which is stable on illumination variation because it is a gradient-based descriptor.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. General Terms Image Processing, Computer Vision, Artificial Intelligenceread more
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ARDIS: a Swedish historical handwritten digit dataset
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Comparison of autoencoder and Principal Component Analysis followed by neural network for e-learning using handwritten recognition
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A Study of Moment Based Features on Handwritten Digit Recognition
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TL;DR: A hybrid model of integrating the synergy of two superior classifiers: Convolutional Neural Network (CNN) and Support Vector Machine (SVM) which have proven results in recognizing different types of patterns is presented.