Handwritten Digits Recognition using Deep Convolutional Neural Network: An Experimental Study using EBlearn
This is an incremental study applying existing methods to a standard dataset for digit recognition, with no clear broader problem addressed.
The paper tackled handwritten digit recognition by implementing a deep convolutional neural network using the EBLearn library on the MNIST dataset, achieving classification into 10 digits with unspecified performance metrics.
In this paper, results of an experimental study of a deep convolution neural network architecture which can classify different handwritten digits using EBLearn library 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).