A Synthetic Recipe for OCR

Abstract

Synthetic data generation for optical character recognition (OCR) promises unlimited training data at zero annotation cost. With enough fonts and seed text, we should be able to generate data to train a model that approaches or exceeds the performance with real annotated data. Unfortunately, this is not always the reality. Unconstrained image settings, such as internet memes, scanned web pages, or newspapers, present diverse scripts, fonts, layouts, and complex backgrounds, which cause models trained with synthetic data to break down. In this work, we investigate the synthetic image generation problem on a large multilingual set of unconstrained document images. Our work presents a comprehensive evaluation of the impact of synthetic data attributes on model performance. The results provide a recipe for synthetic data generation that will help guide future research.