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Deep neural network for handwritten Marathi character recognition

Parshuram M. Kamble, Ravindra S. Hegadi


In this work we show that the sparse autoencoder based feature extraction method in combination with deep neural network based classifier can produce enhanced results when applied to Marathi character recognition. However, in pattern recognition tasks, such as the optical character recognition problem, it is difficult to directly convert handwritten character document image into its constituent character data. Here, we proposed classification of handwritten Marathi character using Deep Neural Network (DNN), which is based on two key concepts: autoencoder features and deep neural network architectures. The proposed model extracts the valid patches from character zones using voting schemes. These extracted patches are arranged in row vector and further fed into DNN for training and testing. We evaluated our approach on handwritten numeral and character of two cross datasets. The proposed classification technique has shown higher performance results over both


Sparse Auto-encoder, Handwritten Marathi Character, Deep neural Network, Compound Characters.

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