Handwritten Digits Recognition using Deep Convolutional Neural Network An Experimental Study using EBlearn

Abstract. In this paper, results of an experimental study of a deep con-volution neural network architecture which can classify different hand-written digits using EBLearn library [1] are reported. The purpose of this neural network is to classify input images into 10 different classes or digits (0-9) and to explore new findings. The input dataset used consists of digits images of size 32X32 in grayscale (MNIST dataset [2])