  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.
