Handwritten text and scene text suffer from various shapes and distorted
patterns. Thus training a robust recognition model requires a large amount of
data to cover diversity as much as possible. In contrast to data collection and
annotation, data augmentation is a low cost way. In this paper, we propose a
new method for text image augmentation. Different from traditional augmentation
methods such as rotation, scaling and perspective transformation, our proposed
augmentation method is designed to learn proper and efficient data augmentation
which is more effective and specific for training a robust recognizer. By using
a set of custom fiducial points, the proposed augmentation method is flexible
and controllable. Furthermore, we bridge the gap between the isolated processes
of data augmentation and network optimization by joint learning. An agent
network learns from the output of the recognition network and controls the
fiducial points to generate more proper training samples for the recognition
network. Extensive experiments on various benchmarks, including regular scene
text, irregular scene text and handwritten text, show that the proposed
augmentation and the joint learning methods significantly boost the performance
of the recognition networks. A general toolkit for geometric augmentation is
available.

本文提出了一种新的文本图像增强方法，使用自定义的参考点来生成更适合训练识别网络的样本，并通过联合学习框架优化训练过程，实验证明该方法显著提高了识别网络的性能。