A Comparative Study of Engraved-Digit Data Augmentation by Generative Adversarial Networks

Abstract

In cases where an efficient information retrieval (IR) system retrieves information from images with engraved digits, as found on medicines, creams, ointments, and gels in squeeze tubes, the system needs to be trained on a large dataset. One of the system applications is to automatically retrieve the expiry date to ascertain the efficacy of the medicine. For expiry dates expressed in engraved digits, it is difficult to collect the digit images. In our study, we evaluated the augmentation performance for a limited, engraved-digit dataset using various generative adversarial networks (GANs). Our study contributes to the choice of an effective GAN for engraved-digit image data augmentation. We conclude that Wasserstein GAN with a gradient norm penalty (WGAN-GP) is a suitable data augmentation technique to address the challenge of producing a large, realistic, but synthetic dataset. Our results show that the stability of WGAN-GP aids in the production of high-quality data with an average Fréchet inception distance (FID) value of 1.5298 across images of 10 digits (0–9) that are nearly indistinguishable from our original dataset.