Random Blur Data Augmentation for Scene Text Recognition

Abstract

In this paper, we propose to apply data augmentation approaches that provide more diverse training images, thus helping train more robust deep models for the Scene Text Recognition (STR) task. The data augmentation methods are Random Blur Region (RBR) and Random Blur Units (RBUs). Specifically, we first introduce RBR designed for the STR task. In training, RBR randomly selects a region and sets the pixels in this region with an average value. However, when RBR provides more various training samples for STR, it may make the samples ambiguous and reduce the recognition accuracy. To address the above problem, we also propose RBUs that divides the blur region into several units. Note that the pixels of one unit share the same value. In this way, RBUs can provide additional readable training samples and help train more robust deep models. Extensive experiments on several STR datasets show that RBUs achieve highly competitive performance. Besides, RBUs are complementary to commonly used data augmentation techniques.