Deep neural network for handwritten Marathi character recognition
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
Regarding indexing issue:
We have provided the online access of all issues & papers to the all indexing agencies (as given on our journal home web site). It’s depend on indexing agencies when, how and what manner they can index or not. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.