Image augmentation by blocky artifact in Deep Convolutional Neural Network for handwritten digit recognition

Abstract

Deep Convolutional Neural Networks - also known as DCNN - are powerful models for different visual pattern classification problems. Many works in this field use image augmentation at the training phase to achieve better accuracy. This paper presents blocky artifact as an augmentation technique to increase the accuracy of DCNN for handwritten digit recognition, both English and Bangla digits, i.e., 0–9. This paper conducts a number of experiments on three different datasets: MNIST Dataset, CMATERDB 3.1.1 Dataset and Indian Statistical Institute (ISI) Dataset. For each dataset, DCNNs with the proposed augmentation technique give better results than those without such augmentation. Unsupervised pre-training with the blocky artifact achieves 99.56%, 99.83% and 99.35% accuracy respectively on MNIST, CMATERDDB and ISI datasets producing, in the process, so far the best accuracy rate for CMATERDB and ISI datasets.