Scene text recognition (STR) is very challenging due to the diversity of text
instances and the complexity of scenes. The community has paid increasing
attention to boost the performance by improving the pre-processing image
module, like rectification and deblurring, or the sequence translator. However,
another critical module, i.e., the feature sequence extractor, has not been
extensively explored. In this work, inspired by the success of neural
architecture search (NAS), which can identify better architectures than
human-designed ones, we propose automated STR (AutoSTR) to search
data-dependent backbones to boost text recognition performance. First, we
design a domain-specific search space for STR, which contains both choices on
operations and constraints on the downsampling path. Then, 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.

本文提出了 AutoSTR 来搜索依赖于数据的骨干以增强文本识别性能，实验表明，通过搜索数据依赖的骨干，AutoSTR 可以在标准基准测试中胜过最先进的方法，且 FLOPS 和模型参数更少。