  Scene text recognition (STR) is the task of recognizing character sequences
in natural scenes. While there have been great advances in STR methods, current
methods still fail to recognize texts in arbitrary shapes, such as heavily
curved or rotated texts, which are abundant in daily life (e.g. restaurant
signs, product labels, company logos, etc). This paper introduces a novel
architecture to recognizing texts of arbitrary shapes, named Self-Attention
Text Recognition Network (SATRN), which is inspired by the Transformer. SATRN
utilizes the self-attention mechanism to describe two-dimensional (2D) spatial
dependencies of characters in a scene text image. Exploiting the full-graph
propagation of self-attention, SATRN can recognize texts with arbitrary
arrangements and large inter-character spacing. As a result, SATRN outperforms
existing STR models by a large margin of 5.7 pp on average in "irregular text"
benchmarks. We provide empirical analyses that illustrate the inner mechanisms
and the extent to which the model is applicable (e.g. rotated and multi-line
text). We will open-source the code.
