  Scene text recognition is a hot research topic in computer vision. Recently,
many recognition methods based on the encoder-decoder framework have been
proposed, and they can handle scene texts of perspective distortion and curve
shape. Nevertheless, they still face lots of challenges like image blur, uneven
illumination, and incomplete characters. We argue that most encoder-decoder
methods are based on local visual features without explicit global semantic
information. In this work, we propose a semantics enhanced encoder-decoder
framework to robustly recognize low-quality scene texts. The semantic
information is used both in the encoder module for supervision and in the
decoder module for initializing. In particular, the state-of-the art ASTER
method is integrated into the proposed framework as an exemplar. Extensive
experiments demonstrate that the proposed framework is more robust for
low-quality text images, and achieves state-of-the-art results on several
benchmark datasets.
