Augmented Handwritten Devanagari Digit Recognition Using Convolutional Autoencoder
Sourabh Kumar, Rajesh Kumar Aggarwal · 2018 International Conference on Inventive Research in Computing Applications (ICIRCA) · 2018
Handwritten digit recognition has turned into one of the demanding areas of research in the field of image processing. Many approaches have been proposed which include a statistical method, fuzzy technique, and neural network for feature classification and feature selection but have not been found to use convolutional autoencoder for Devanagari digit after performing image augmentation on the training dataset. This paper shows the use of unsupervised training using convolutional autoencoder with deep ConvNet in order to detect handwritten Devanagari digits, i.e., 0-9. Convolutional autoencoder is the type of autoencoder that is used to encode the input for extracting important features and then try to reconstruct the input image. This paper shows the improved accuracy of Hindi, English and Bangla digit dataset by using the proposed approach and also performing a number of cross-validation experiments on all three datasets using image augmentation.