Variational Autoencoder Coupled with Deep Generative Neural Network for the Identification of Handwritten Digits
In the present paper, a novel approach of machine learning technique has been implemented that will help the machine to learn from the experience and understand hand written digits using Deep Generative Neural Network (DGNN) The current work describes a process to generate unique images and recognize the hand written digits sent as input image to the proposed model by using unsupervised learning. In the proposed method, a variational auto encoder (VAE) has been utilized to encode and decode the input image. Inside an auto encoder, there consists of several hidden layers 'h' which represents the input. Then clustering is undertaken and the result of clustering reflects. loss of information, so an Convolutional Neural network (CNN) lossy function is adopted to retrieve the lost information and finally DGNN is employed to predict the output and a digit generator is utilized to produce images of digits(0 9) as result of the model. Clustering of digits has been performed to show the similarities and dissimilarities among the different digits contained in the data set. Manually handwritten digits can be identified very easily but it is tough and challenging for machines to understand because handwriting differs from person to person. Although it is difficult to achieve, but it has been observed that DGNN produces acceptable results and performs identification of handwritten digits (taken input as no label data) in a better manner.