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Handwritten text and scene text suffer from various shapes and distortedpatterns. Thus training a robust recognition model requires a large amount ofdata to cover diversity as much as possible. In contrast to data collection andannotation, data augmentation is a low cost way. In this paper, we propose anew method for text image augmentation. Different from traditional augmentationmethods such as rotation, scaling and perspective transformation, our proposedaugmentation method is designed to learn proper and efficient data augmentationwhich is more effective and specific for training a robust recognizer. By usinga set of custom fiducial points, the proposed augmentation method is flexibleand controllable. Furthermore, we bridge the gap between the isolated processesof data augmentation and network optimization by joint learning. An agentnetwork learns from the output of the recognition network and controls thefiducial points to generate more proper training samples for the recognitionnetwork. Extensive experiments on various benchmarks, including regular scenetext, irregular scene text and handwritten text, show that the proposedaugmentation and the joint learning methods significantly boost the performanceof the recognition networks. A general toolkit for geometric augmentation isavailable.

Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition