  The attention-based encoder-decoder framework has recently achieved
impressive results for scene text recognition, and many variants have emerged
with improvements in recognition quality. However, it performs poorly on
contextless texts (e.g., random character sequences) which is unacceptable in
most of real application scenarios. In this paper, we first deeply investigate
the decoding process of the decoder. We empirically find that a representative
character-level sequence decoder utilizes not only context information but also
positional information. Contextual information, which the existing approaches
heavily rely on, causes the problem of attention drift. To suppress such
side-effect, we propose a novel position enhancement branch, and dynamically
fuse its outputs with those of the decoder attention module for scene text
recognition. Specifically, it contains a position aware module to enable the
encoder to output feature vectors encoding their own spatial positions, and an
attention module to estimate glimpses using the positional clue (i.e., the
current decoding time step) only. The dynamic fusion is conducted for more
robust feature via an element-wise gate mechanism. Theoretically, our proposed
method, dubbed \emph{RobustScanner}, decodes individual characters with dynamic
ratio between context and positional clues, and utilizes more positional ones
when the decoding sequences with scarce context, and thus is robust and
practical. Empirically, it has achieved new state-of-the-art results on popular
regular and irregular text recognition benchmarks while without much
performance drop on contextless benchmarks, validating its robustness in both
contextual and contextless application scenarios.
