  Attention-based scene text recognizers have gained huge success, which
leverages a more compact intermediate representation to learn 1d- or 2d-
attention by a RNN-based encoder-decoder architecture. However, such methods
suffer from attention-drift problem because high similarity among encoded
features leads to attention confusion under the RNN-based local attention
mechanism. Moreover, RNN-based methods have low efficiency due to poor
parallelization. To overcome these problems, we propose the MASTER, a
self-attention based scene text recognizer that (1) not only encodes the
input-output attention but also learns self-attention which encodes
feature-feature and target-target relationships inside the encoder and decoder
and (2) learns a more powerful and robust intermediate representation to
spatial distortion, and (3) owns a great training efficiency because of high
training parallelization and a high-speed inference because of an efficient
memory-cache mechanism. Extensive experiments on various benchmarks demonstrate
the superior performance of our MASTER on both regular and irregular scene
text. Pytorch code can be found at https://github.com/wenwenyu/MASTER-pytorch,
and Tensorflow code can be found at https://github.com/jiangxiluning/MASTER-TF.
