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Performance Comparison of SVM and ANN for Handwritten Devnagari Character Recognition
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TLDR
The characteristics of the some classification methods that have been successfully applied to handwritten Devnagari character recognition and results of SVM and ANNs classification method, applied on Handwritten DevNagari characters are discussed.Abstract:
Classification methods based on learning from examples have been widely applied to character recognition from the 1990s and have brought forth significant improvements of recognition accuracies This class of methods includes statistical methods, artificial neural networks, support vector machines (SVM), multiple classifier combination, etc In this paper, we discuss the characteristics of the some classification methods that have been successfully applied to handwritten Devnagari character recognition and results of SVM and ANNs classification method, applied on Handwritten Devnagari characters After preprocessing the character image, we extracted shadow features, chain code histogram features, view based features and longest run features These features are then fed to Neural classifier and in support vector machine for classification In neural classifier, we explored three ways of combining decisions of four MLP’s, designed for four different featuresread more
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A Review of Research on Devnagari Character Recognition
Vikas J. Dongre,Vijay H. Mankar +1 more
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References
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Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
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Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
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Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.