Modulai Blog

Introduction

Synthetic data generation has in recent years emerged as an alternative way to manually gather and annotate data sets. The advantages of a data synthesizer are mainly two-fold. Firstly, it drives the cost of acquiring data to almost zero while eliminating almost all data processing work. Secondly, it has the potential to enable quick prototyping of new machine learning ideas even when data is not available. To put it simply, a well-constructed synthetic data generator produces data in a ready-to-go format for any machine learning case. However, the challenge with synthetic data generators is that they are often domain-specific with a narrow application area and far less mature than methods for network architecture and learning.

In the field of computer vision, synthetic data generation is especially interesting since the number of relevant resources and tools has grown and improved significantly over the years. The development has not been in the field of machine learning, though, but rather in game engines such as Unreal Engine, Blender, and Unity. Often produced by professional designers, realistic scenes are produced offering great details as visualized in the video below.

Modulai has deployed models trained on synthetic data in several projects and now seeks to understand the potential of 3D rendered data for solving computer vision tasks. Specifically, in this project, the task is to generate synthetic data images consisting of text using Unreal Engine. The data will be used to train a network with the aim of detecting text in images captured from real environments.

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