AutoSTR: Efficient Backbone Search for Scene Text Recognition

Abstract

Scene text recognition (STR) is challenging due to the diversity of text instances and the complexity of scenes. However, no STR methods can adapt backbones to different diversities and complexities. In this work, inspired by the success of neural architecture search (NAS), we propose automated STR (AutoSTR), which can address the above issue by searching data-dependent backbones. Specifically, we show both choices on operations and the downsampling path are very important in the search space design of NAS. Besides, since no existing NAS algorithms can handle the spatial constraint on the path, we propose a two-step search algorithm, which decouples operations and downsampling path, for an efficient search in the given space. Experiments demonstrate that, by searching data-dependent backbones, AutoSTR can outperform the state-of-the-art approaches on standard benchmarks with much fewer FLOPS and model parameters. (Code is available at https://github.com/AutoML-4Paradigm/AutoSTR.git).