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 this https URL,
and Tensorflow code can be found at this https URL

提出了一种基于自注意力的场景文本识别器 ——MASTER。相比于基于 RNN 的编码器 - 解码器体系结构，该模型通过学习 1d 或 2d 注意力来利用更紧凑的中间表示，在编码器和解码器内部学习自我注意力，从而解决了关注漂移和低效率的问题。